Journal articles on the topic 'Evolutionary Algorithms (EAs)'
To see the other types of publications on this topic, follow the link: Evolutionary Algorithms (EAs).
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Evolutionary Algorithms (EAs).'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Mashwani, Wali Khan, Zia Ur Rehman, Maharani A. Bakar, Ismail Koçak, and Muhammad Fayaz. "A Customized Differential Evolutionary Algorithm for Bounded Constrained Optimization Problems." Complexity 2021 (March 10, 2021): 1–24. http://dx.doi.org/10.1155/2021/5515701.
Full textAbstract:
Bound-constrained optimization has wide applications in science and engineering. In the last two decades, various evolutionary algorithms (EAs) were developed under the umbrella of evolutionary computation for solving various bound-constrained benchmark functions and various real-world problems. In general, the developed evolutionary algorithms (EAs) belong to nature-inspired algorithms (NIAs) and swarm intelligence (SI) paradigms. Differential evolutionary algorithm is one of the most popular and well-known EAs and has secured top ranks in most of the EA competitions in the special session of the IEEE Congress on Evolutionary Computation. In this paper, a customized differential evolutionary algorithm is suggested and applied on twenty-nine large-scale bound-constrained benchmark functions. The suggested C-DE algorithm has obtained promising numerical results in its 51 independent runs of simulations. Most of the 2013 IEEE-CEC benchmark functions are tackled efficiently in terms of proximity and diversity.
2
Dao, Tran Trong. "Investigation on Evolutionary Computation Techniques of a Nonlinear System." Modelling and Simulation in Engineering 2011 (2011): 1–21. http://dx.doi.org/10.1155/2011/496732.
Full textAbstract:
The main aim of this work is to show that such a powerful optimizing tool like evolutionary algorithms (EAs) can be in reality used for the simulation and optimization of a nonlinear system. A nonlinear mathematical model is required to describe the dynamic behaviour of batch process; this justifies the use of evolutionary method of the EAs to deal with this process. Four algorithms from the field of artificial intelligent—differential evolution (DE), self-organizing migrating algorithm (SOMA), genetic algorithm (GA), and simulated annealing (SA)—are used in this investigation. The results show that EAs are used successfully in the process optimization.
3
Sivadasan, J., M. Willjuice Iruthayarajan, Albert Alexander Stonier, and A. Raymon. "Design of Cross-Coupled Nonlinear PID Controller Using Single-Objective Evolutionary Algorithms." Mathematical Problems in Engineering 2023 (April 15, 2023): 1–13. http://dx.doi.org/10.1155/2023/7820047.
Full textAbstract:
The effectiveness of evolutionary algorithms (EAs) such as differential search algorithm (DSA), Real-Coded genetic algorithm with simulated binary crossover (RGA-SBX), particle swarm optimization (PSO), and chaotic gravitational search algorithm (CGSA) on the optimal design of cross-coupled nonlinear PID controllers is compared in this paper. A cross-coupled multivariable PID controller structure for the binary distillation column was developed with two inputs and two outputs. EA simulations are run to lower IAE using two stopping criteria, namely, maximum number of functional evaluations (Fevalmax) and Fevalmax plus PID parameter and IAE tolerance. Over 20 separate trials were used to compare the performances of several EAs using statistical measures such as best, mean, standard deviation of outcomes, and average calculation time. This article presents the design of a cross-coupled nonlinear PID controller using single-objective evolutionary algorithms. Using evolutionary algorithms (EAs) with a multicrossover strategy, the results achieved by various EAs are compared to previously reported results. The results of a multivariable cross-coupled system clearly show that a single-objective nonlinear PID controller performs better. Simulations further show that all four techniques evaluated are suitable for PID controller tweaking off-line. However, only the single-objective evolutionary algorithms are acceptable for online PID tuning due to their higher consistency and shorter computation time.
4
Bäck, Thomas, and Hans-Paul Schwefel. "An Overview of Evolutionary Algorithms for Parameter Optimization." Evolutionary Computation 1, no. 1 (March 1993): 1–23. http://dx.doi.org/10.1162/evco.1993.1.1.1.
Full textAbstract:
Three main streams of evolutionary algorithms (EAs), probabilistic optimization algorithms based on the model of natural evolution, are compared in this article: evolution strategies (ESs), evolutionary programming (EP), and genetic algorithms (GAs). The comparison is performed with respect to certain characteristic components of EAs: the representation scheme of object variables, mutation, recombination, and the selection operator. Furthermore, each algorithm is formulated in a high-level notation as an instance of the general, unifying basic algorithm, and the fundamental theoretical results on the algorithms are presented. Finally, after presenting experimental results for three test functions representing a unimodal and a multimodal case as well as a step function with discontinuities, similarities and differences of the algorithms are elaborated, and some hints to open research questions are sketched.
5
Heredia, Jorge Pérez. "Modelling Evolutionary Algorithms with Stochastic Differential Equations." Evolutionary Computation 26, no. 4 (December 2018): 657–86. http://dx.doi.org/10.1162/evco_a_00216.
Full textAbstract:
There has been renewed interest in modelling the behaviour of evolutionary algorithms (EAs) by more traditional mathematical objects, such as ordinary differential equations or Markov chains. The advantage is that the analysis becomes greatly facilitated due to the existence of well established methods. However, this typically comes at the cost of disregarding information about the process. Here, we introduce the use of stochastic differential equations (SDEs) for the study of EAs. SDEs can produce simple analytical results for the dynamics of stochastic processes, unlike Markov chains which can produce rigorous but unwieldy expressions about the dynamics. On the other hand, unlike ordinary differential equations (ODEs), they do not discard information about the stochasticity of the process. We show that these are especially suitable for the analysis of fixed budget scenarios and present analogues of the additive and multiplicative drift theorems from runtime analysis. In addition, we derive a new more general multiplicative drift theorem that also covers non-elitist EAs. This theorem simultaneously allows for positive and negative results, providing information on the algorithm's progress even when the problem cannot be optimised efficiently. Finally, we provide results for some well-known heuristics namely Random Walk (RW), Random Local Search (RLS), the (1+1) EA, the Metropolis Algorithm (MA), and the Strong Selection Weak Mutation (SSWM) algorithm.
6
Chen, Yaxin, Xin Shen, Guo Zhang, and Zezhong Lu. "Multi-Objective Multi-Satellite Imaging Mission Planning Algorithm for Regional Mapping Based on Deep Reinforcement Learning." Remote Sensing 15, no. 16 (August 8, 2023): 3932. http://dx.doi.org/10.3390/rs15163932.
Full textAbstract:
Satellite imaging mission planning is used to optimize satellites to obtain target images efficiently. Many evolutionary algorithms (EAs) have been proposed for satellite mission planning. EAs typically require evolutionary parameters, such as the crossover and mutation rates. The performance of EAs is considerably affected by parameter setting. However, most parameter configuration methods of the current EAs are artificially set and lack the overall consideration of multiple parameters. Thus, parameter configuration becomes suboptimal and EAs cannot be effectively utilized. To obtain satisfactory optimization results, the EA comp ensates by extending the evolutionary generation or improving the evolutionary strategy, but it significantly increases the computational consumption. In this study, a multi-objective learning evolutionary algorithm (MOLEA) was proposed to solve the optimal configuration problem of multiple evolutionary parameters and used to solve effective imaging satellite task planning for region mapping. In the MOLEA, population state encoding provided comprehensive population information on the configuration of evolutionary parameters. The evolutionary parameters of each generation were configured autonomously through deep reinforcement learning (DRL), enabling each generation of parameters to gain the best evolutionary benefits for future evolution. Furthermore, the HV of the multi-objective evolutionary algorithm (MOEA) was used to guide reinforcement learning. The superiority of the proposed MOLEA was verified by comparing the optimization performance, stability, and running time of the MOLEA with existing multi-objective optimization algorithms by using four satellites to image two regions of Hubei and Congo (K). The experimental results showed that the optimization performance of the MOLEA was significantly improved, and better imaging satellite task planning solutions were obtained.
7
Mashwani, Wali Khan, Ruqayya Haider, and Samir Brahim Belhaouari. "A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems." Complexity 2021 (February 27, 2021): 1–18. http://dx.doi.org/10.1155/2021/5521951.
Full textAbstract:
Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- (SI-) based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO), and bat algorithm (BA) have gained much popularity and they have successfully tackled various test suites of benchmark functions and real-world problems. These SI-based algorithms follow the social and interactive principles to perform their search process while approximating solution for the given problems. In this paper, a multiswarm-intelligence-based algorithm (MSIA) is developed to cope with bound constrained functions. The suggested algorithm integrates the SI-based algorithms to evolve population and handle exploration versus exploitation issues. Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. The test suite of benchmark function is recently designed for the special session of EAs competition in IEEE Congress on Evolutionary Computation (IEEE-CEC′13). The suggested algorithm has approximated promising solutions with good convergence and diversity maintenance for most of the used bound constrained single optimization problems.
8
Dang, Duc-Cuong, Anton Eremeev, and Per Kristian Lehre. "Escaping Local Optima with Non-Elitist Evolutionary Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 12275–83. http://dx.doi.org/10.1609/aaai.v35i14.17457.
Full textAbstract:
Most discrete evolutionary algorithms (EAs) implement elitism, meaning that they make the biologically implausible assumption that the fittest individuals never die. While elitism favours exploitation and ensures that the best seen solutions are not lost, it has been widely conjectured that non-elitism is necessary to explore promising fitness valleys without getting stuck in local optima. Determining when non-elitist EAs outperform elitist EAs has been one of the most fundamental open problems in evolutionary computation. A recent analysis of a non-elitist EA shows that this algorithm does not outperform its elitist counterparts on the benchmark problem JUMP. We solve this open problem through rigorous runtime analysis of elitist and non-elitist population-based EAs on a class of multi-modal problems. We show that with 3-tournament selection and appropriate mutation rates, the non-elitist EA optimises the multi-modal problem in expected polynomial time, while an elitist EA requires exponential time with overwhelmingly high probability. A key insight in our analysis is the non-linear selection profile of the tournament selection mechanism which, with appropriate mutation rates, allows a small sub-population to reside on the local optimum while the rest of the population explores the fitness valley. In contrast, we show that the comma-selection mechanism which does not have this non-linear profile, fails to optimise this problem in polynomial time. The theoretical analysis is complemented with an empirical investigation on instances of the set cover problem, showing that non-elitist EAs can perform better than the elitist ones. We also provide examples where usage of mutation rates close to the error thresholds is beneficial when employing non-elitist population-based EAs.
9
ABBASS, H. A., and R. SARKER. "THE PARETO DIFFERENTIAL EVOLUTION ALGORITHM." International Journal on Artificial Intelligence Tools 11, no. 04 (December 2002): 531–52. http://dx.doi.org/10.1142/s0218213002001039.
Full textAbstract:
The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Vector Optimization Problems (VOPs)) has attracted much attention recently. Being population based approaches, EAs offer a means to find a group of pareto-optimal solutions in a single run. Differential Evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. The objective of this paper is to introduce a novel Pareto Differential Evolution (PDE) algorithm to solve VOPs. The solutions provided by the proposed algorithm for five standard test problems, is competitive to nine known evolutionary multiobjective algorithms for solving VOPs.
10
Fonseca, Carlos M., and Peter J. Fleming. "An Overview of Evolutionary Algorithms in Multiobjective Optimization." Evolutionary Computation 3, no. 1 (March 1995): 1–16. http://dx.doi.org/10.1162/evco.1995.3.1.1.
Full textAbstract:
The application of evolutionary algorithms (EAs) in multiobjective optimization is currently receiving growing interest from researchers with various backgrounds. Most research in this area has understandably concentrated on the selection stage of EAs, due to the need to integrate vectorial performance measures with the inherently scalar way in which EAs reward individual performance, that is, number of offspring. In this review, current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking schemes based on the definition of Pareto optimality. The sensitivity of different methods to objective scaling and/or possible concavities in the trade-off surface is considered, and related to the (static) fitness landscapes such methods induce on the search space. From the discussion, directions for future research in multiobjective fitness assignment and search strategies are identified, including the incorporation of decision making in the selection procedure, fitness sharing, and adaptive representations.
11
K, Devika, and Guruswamy Jeyakumar. "Theoretical Analysis and Empirical Comparison of Different Population Initialization Techniques for Evolutionary Algorithms." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 1 (October 1, 2018): 87. http://dx.doi.org/10.11591/ijeecs.v12.i1.pp87-94.
Full textAbstract:
<p class="Abstract">Evolutionary Algorithms (EAs) are the potential tools for solving optimization problems. The EAs are the population based algorithms and they search for the optimal solution(s) from an initial set of candidates solutions known as population. This population is to be initialized at first before the evolution of the algorithm starts. There exists different ways to initialize this population. Understanding and choosing the right population initialization technique for the given problem is a difficult task for the researchers and problem solvers. To alleviate this issue, this paper is framed with two objectives. The first objective is to present the details of various Population Initialization (PI) techniques of EAs, for the readers to give brief description of all the PI techniques. The second objective is to present the steps and empirical comparison of the results of two different PI techniques implemented for Differential Evolution (DE) algorithm. Theoretical insights and empirical results of the PI techniques are presented in this paper.</p>
12
Ter-Sarkisov, Aram, and Stephen Marsland. "Convergence Properties of (μ + λ) Evolutionary Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 1816–17. http://dx.doi.org/10.1609/aaai.v25i1.8037.
Full textAbstract:
We present a number of convergence properties of population-based Evolutionary Algorithms (EAs) on a set of test functions. Focus is on EA using k-Bit-Swap (kBS) operator. We compare our findings to past research.
13
Lai, Xinsheng, Yuren Zhou, Xiaoyun Xia, and Qingfu Zhang. "Performance Analysis of Evolutionary Algorithms for Steiner Tree Problems." Evolutionary Computation 25, no. 4 (December 2017): 707–23. http://dx.doi.org/10.1162/evco_a_00200.
Full textAbstract:
The Steiner tree problem (STP) aims to determine some Steiner nodes such that the minimum spanning tree over these Steiner nodes and a given set of special nodes has the minimum weight, which is NP-hard. STP includes several important cases. The Steiner tree problem in graphs (GSTP) is one of them. Many heuristics have been proposed for STP, and some of them have proved to be performance guarantee approximation algorithms for this problem. Since evolutionary algorithms (EAs) are general and popular randomized heuristics, it is significant to investigate the performance of EAs for STP. Several empirical investigations have shown that EAs are efficient for STP. However, up to now, there is no theoretical work on the performance of EAs for STP. In this article, we reveal that the (1+1) EA achieves 3/2-approximation ratio for STP in a special class of quasi-bipartite graphs in expected runtime [Formula: see text], where [Formula: see text], [Formula: see text], and [Formula: see text] are, respectively, the number of Steiner nodes, the number of special nodes, and the largest weight among all edges in the input graph. We also show that the (1+1) EA is better than two other heuristics on two GSTP instances, and the (1+1) EA may be inefficient on a constructed GSTP instance.
14
Gayatri, R., and N. Baskar. "Evaluating Process Parameters of Multi-Pass Turning Process Using Hybrid Genetic Simulated Swarm Algorithm." Journal of Advanced Manufacturing Systems 14, no. 04 (September 29, 2015): 215–33. http://dx.doi.org/10.1142/s0219686715500146.
Full textAbstract:
Evolutionary computation is one of the important problems solving method frequently used by the researchers. The choice of an algorithm to optimize the problem is determined by some sort of reliability of the researcher with that technique. To overcome the limitations in individual algorithms and to achieve synergic effects, fusion or hybridization of two or more algorithms is carried out. Hybrid algorithms have gained popularity because there is no evidence that a universal optimal strategy exists for solving optimization problems. In this work, a hybrid algorithm called hybrid genetic simulated swarm (HGSS) algorithm is proposed to optimize the parameters of multi-pass turning operation. The HGSS algorithm is a fusion of genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO) algorithms. The objectives of this work are (i) to explore and exploit the problem search space through hybridization, (ii) to justify that proficient hybridization of evolutionary algorithms (EAs) will yield an efficient means to solve the optimization problems. In this work, the EAs such as GA, SA and PSO are also applied to optimize parameters and results are compared with HGSS. The results of the proposed work HGSS are very effective than other algorithms.
15
Lin, Yung Chien. "A Memetic Algorithm for Mixed-Integer Optimization Problems." Applied Mechanics and Materials 284-287 (January 2013): 2970–74. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2970.
Full textAbstract:
Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and tested on a benchmark mixed-integer constrained optimization problem. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.
16
Qian, Chao, Yang Yu, and Zhi-Hua Zhou. "Analyzing Evolutionary Optimization in Noisy Environments." Evolutionary Computation 26, no. 1 (March 2018): 1–41. http://dx.doi.org/10.1162/evco_a_00170.
Full textAbstract:
Many optimization tasks must be handled in noisy environments, where the exact evaluation of a solution cannot be obtained, only a noisy one. For optimization of noisy tasks, evolutionary algorithms (EAs), a type of stochastic metaheuristic search algorithm, have been widely and successfully applied. Previous work mainly focuses on the empirical study and design of EAs for optimization under noisy conditions, while the theoretical understandings are largely insufficient. In this study, we first investigate how noisy fitness can affect the running time of EAs. Two kinds of noise-helpful problems are identified, on which the EAs will run faster with the presence of noise, and thus the noise should not be handled. Second, on a representative noise-harmful problem in which the noise has a strong negative effect, we examine two commonly employed mechanisms dealing with noise in EAs: reevaluation and threshold selection. The analysis discloses that using these two strategies simultaneously is effective for the one-bit noise but ineffective for the asymmetric one-bit noise. Smooth threshold selection is then proposed, which can be proved to be an effective strategy to further improve the noise tolerance ability in the problem. We then complement the theoretical analysis by experiments on both synthetic problems as well as two combinatorial problems, the minimum spanning tree and the maximum matching. The experimental results agree with the theoretical findings and also show that the proposed smooth threshold selection can deal with the noise better.
17
Bi, Weiwei, Yihui Xu, and Hongyu Wang. "Comparison of Searching Behaviour of Three Evolutionary Algorithms Applied to Water Distribution System Design Optimization." Water 12, no. 3 (March 3, 2020): 695. http://dx.doi.org/10.3390/w12030695.
Full textAbstract:
Over the past few decades, various evolutionary algorithms (EAs) have been applied to the optimization design of water distribution systems (WDSs). An important research area is to compare the performance of these EAs, thereby offering guidance for the selection of the appropriate EAs for practical implementations. Such comparisons are mainly based on the final solution statistics and, hence, are unable to provide knowledge on how different EAs reach the final optimal solutions and why different EAs performed differently in identifying optimal solutions. To this end, this paper aims to compare the real-time searching behaviour of three widely used EAs, which are genetic algorithms (GAs), the differential evolution (DE) algorithm and the ant colony optimization (ACO). These three EAs are applied to five WDS benchmarking case studies with different scales and complexities, and a set of five metrics are used to measure their run-time searching quality and convergence properties. Results show that the run-time metrics can effectively reveal the underlying searching mechanisms associated with each EA, which significantly goes beyond the knowledge from the traditional end-of-run solution statistics. It is observed that the DE is able to identify better solutions if moderate and large computational budgets are allowed due to its great ability in maintaining the balance between the exploration and exploitation. However, if the computational resources are rather limited or the decision has to be made in a very short time (e.g., real-time WDS operation), the GA can be a good choice as it can always identify better solutions than the DE and ACO at the early searching stages. Based on the results, the ACO performs the worst for the five case study considered. The outcome of this study is the offer of guidance for the algorithm selection based on the available computation resources, as well as knowledge into the EA’s underlying searching behaviours.
18
Chen, Ming, Yunwen Lei, Lixin Ding, and Zhao Tong. "Convergence in Probability on a Big Class of Time-Variant Evolutionary Algorithms." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 06 (April 21, 2019): 1959018. http://dx.doi.org/10.1142/s0218001419590183.
Full textAbstract:
Motivated by the growing popularity of time-variant evolutionary algorithms (EAs) in solving practical problems, this paper uses spectral analyses to study convergence in probability for a general class of time-variant EAs which can be asymptotically described by reducible Markov chains with multiple aperiodic recurrent classes, covering many existing concrete case studies as specific instantiations. We provide a universal yet easily checkable characteristic for time-variant EAs satisfying global convergence, by introducing the asymptotical elitism and asymptotical monotonicity. To illustrate the effectiveness of our result, we consider four specific EAs with distinct asymptotical behavior, and recover, under even mild conditions, the state-of-the-art result as simple applications of our general theorem. Besides, simulation experiments further verify these results.
19
Hashem, M. M. A., Keigo Watanabe, and Kiyotaka Izumi. "Stable-Optimum Gain Tuning for Designing Mobile Robot Controllers Using Incest Prevented Evolution." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 5 (October 20, 1998): 164–75. http://dx.doi.org/10.20965/jaciii.1998.p0164.
Full textAbstract:
We present an evolution strategy (ES) algorithm - incest prevented evolution strategy (IPES) enhancing our novel evolution strategy (NES) algorithm. Validity of NES and IPES algorithms is compared with other evolutionary algorithms (EAs) and relative performances and also compared with test function results. The IPES algorithm shows the highest balance between exploration and exploitation over the NES algorithm on these test functions by achieving high-precision global results. Both algorithms are applied to solve stabilizing optimum gain tuning problems in mobile robot controllers. Two optimal servocontrollers are considered for a mobile robot with two independent drive wheels. A bidirectional fitness (cost) function is constructed for these controllers so that stable but optimum gains are tuned automatically evolutionarily instead of using a traditional algebraic Riccati equation solution. Two trajectory tracking control examples (straight line and circular) are considered for controllers. The superiority of the IPES algorithm over the NES algorithm is repeated in the application domain and the effectiveness of evolutionary gain tuning demonstrated by simulation results.
20
Radhakrishna Prabhu, Shanker G., Richard C. Seals, Peter J. Kyberd, and Jodie C. Wetherall. "A survey on evolutionary-aided design in robotics." Robotica 36, no. 12 (August 17, 2018): 1804–21. http://dx.doi.org/10.1017/s0263574718000747.
Full textAbstract:
SUMMARYThe evolutionary-aided design process is a method to find solutions to design and optimisation problems. Evolutionary algorithms (EAs) are applied to search for optimal solutions from a solution space that evolves over several generations. EAs have found applications in many areas of robotics. This paper covers the efforts to determine body morphology of robots through evolution and body morphology with the controller of robots or similar creatures through co-evolution. The works are reviewed from the perspective of how different algorithms are applied and includes a brief explanation of how they are implemented.
21
Salomon, Ralf. "Some Comments on Evolutionary Algorithm Theory." Evolutionary Computation 4, no. 4 (December 1996): 405–15. http://dx.doi.org/10.1162/evco.1996.4.4.405.
Full textAbstract:
The development of a sound theory that predicts and verifies existing evolutionary algorithms (EA) is one of the most important research issues in the field today. In mathematical proofs, the assumption of spherical symmetry is probably one of the most widely used simplifications. This paper discusses the extent to which spherical symmetry is appropriate for certain EAs. It turns out that spherical symmetry leads to simplifications in (self-adaptive) EAs but seems inappropriate for certain genetic algorithm variants, since small mutation rates bias a search algorithm toward the coordinate axes. This paper also argues that current test suites are weak in that they do not provide problems with significant epistasis that describes the interaction between different parameters. Consequently, when using an empirical test for pushing existing theory beyond its limits, benchmark functions should include more epistatic interaction or at least should use coordinate rotations.
22
Lin, Yung Chien. "A Mixed-Integer Memetic Algorithm Applied to Batch Process Optimization." Applied Mechanics and Materials 300-301 (February 2013): 645–48. http://dx.doi.org/10.4028/www.scientific.net/amm.300-301.645.
Full textAbstract:
Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a mixed-integer memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and applied to the optimal design of batch processes. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.
23
Goudos, Sotirios K., Christos Kalialakis, and Raj Mittra. "Evolutionary Algorithms Applied to Antennas and Propagation: A Review of State of the Art." International Journal of Antennas and Propagation 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/1010459.
Full textAbstract:
A review of evolutionary algorithms (EAs) with applications to antenna and propagation problems is presented. EAs have emerged as viable candidates for global optimization problems and have been attracting the attention of the research community interested in solving real-world engineering problems, as evidenced by the fact that very large number of antenna design problems have been addressed in the literature in recent years by using EAs. In this paper, our primary focus is on Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Differential Evolution (DE), though we also briefly review other recently introduced nature-inspired algorithms. An overview of case examples optimized by each family of algorithms is included in the paper.
24
Droste, Stefan, Thomas Jansen, and Ingo Wegener. "A Rigorous Complexity Analysis of the (1 + 1) Evolutionary Algorithm for Separable Functions with Boolean Inputs." Evolutionary Computation 6, no. 2 (June 1998): 185–96. http://dx.doi.org/10.1162/evco.1998.6.2.185.
Full textAbstract:
Evolutionary algorithms (EAs) are heuristic randomized algorithms which, by many impressive experiments, have been proven to behave quite well for optimization problems of various kinds. In this paper a rigorous theoretical complexity analysis of the (1 + 1) evolutionary algorithm for separable functions with Boolean inputs is given. Different mutation rates are compared, and the use of the crossover operator is investigated. The main contribution is not the result that the expected run time of the (1 + 1) evolutionary algorithm is Θ(n ln n) for separable functions with n variables but the methods by which this result can be proven rigorously.
25
RATLE, ALAIN. "Kriging as a surrogate fitness landscape in evolutionary optimization." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 1 (January 2001): 37–49. http://dx.doi.org/10.1017/s0890060401151024.
Full textAbstract:
The problem of finding optimal values in complex parameter optimization problems has often been solved with success by evolutionary algorithms (EAs). In many cases, these algorithms are employed as black-box methods over imprecisely known domains. Such problems arise frequently in engineering design. The principal barrier to the general use of EAs for those problems is the huge number of function evaluations that is often required. This makes EAs an impractical approach when the function evaluation depends on numerically heavy design analysis tools, for example, finite elements methods. This paper presents the use of kriging interpolation as a function approximation method for the construction of an internal model of the fitness landscape. This model is intended to guide the search process with a reduced number of fitness function evaluations.
26
Yu, Yadong, Haiping Ma, Mei Yu, Sengang Ye, and Xiaolei Chen. "Multipopulation Management in Evolutionary Algorithms and Application to Complex Warehouse Scheduling Problems." Complexity 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/4730957.
Full textAbstract:
Multipopulation is an effective optimization strategy which is often used in evolutionary algorithms (EAs) to improve optimization performance. However, it is of remarkable difficulty to determine the number of subpopulations during the evolution process for a given problem, which may significantly affect optimization ability of EAs. This paper proposes a simple multipopulation management strategy to dynamically adjust the subpopulation number in different evolution phases throughout the evolution. The proposed method makes use of individual distances in the same subpopulation as well as the population distances between multiple subpopulations to determine the subpopulation number, which is substantial in maintaining population diversity and enhancing the exploration ability. Furthermore, the proposed multipopulation management strategy is embedded into popular EAs to solve real-world complex automated warehouse scheduling problems. Experimental results show that the proposed multipopulation EAs can easily be implemented and outperform other regular single-population algorithms to a large extent.
27
SARKER, RUHUL, JOARDER KAMRUZZAMAN, and CHARLES NEWTON. "EVOLUTIONARY OPTIMIZATION (EvOpt): A BRIEF REVIEW AND ANALYSIS." International Journal of Computational Intelligence and Applications 03, no. 04 (December 2003): 311–30. http://dx.doi.org/10.1142/s1469026803001051.
Full textAbstract:
Evolutionary Computation (EC) has attracted increasing attention in recent years, as powerful computational techniques, for solving many complex real-world problems. The Operations Research (OR)/Optimization community is divided on the acceptability of these techniques. One group accepts these techniques as potential heuristics for solving complex problems and the other rejects them on the basis of their weak mathematical foundations. In this paper, we discuss the reasons for using EC in optimization. A brief review of Evolutionary Algorithms (EAs) and their applications is provided. We also investigate the use of EAs for solving a two-stage transportation problem by designing a new algorithm. The computational results are analyzed and compared with conventional optimization techniques.
28
Hu, Jianjun, Erik Goodman, Kisung Seo, Zhun Fan, and Rondal Rosenberg. "The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms." Evolutionary Computation 13, no. 2 (June 2005): 241–77. http://dx.doi.org/10.1162/1063656054088530.
Full textAbstract:
Many current Evolutionary Algorithms (EAs) suffer from a tendency to converge prematurely or stagnate without progress for complex problems. This may be due to the loss of or failure to discover certain valuable genetic material or the loss of the capability to discover new genetic material before convergence has limited the algorithm's ability to search widely. In this paper, the Hierarchical Fair Competition (HFC) model, including several variants, is proposed as a generic framework for sustainable evolutionary search by transforming the convergent nature of the current EA framework into a non-convergent search process. That is, the structure of HFC does not allow the convergence of the population to the vicinity of any set of optimal or locally optimal solutions. The sustainable search capability of HFC is achieved by ensuring a continuous supply and the incorporation of genetic material in a hierarchical manner, and by culturing and maintaining, but continually renewing, populations of individuals of intermediate fitness levels. HFC employs an assembly-line structure in which subpopulations are hierarchically organized into different fitness levels, reducing the selection pressure within each subpopulation while maintaining the global selection pressure to help ensure the exploitation of the good genetic material found. Three EAs based on the HFC principle are tested - two on the even-10-parity genetic programming benchmark problem and a real-world analog circuit synthesis problem, and another on the HIFF genetic algorithm (GA) benchmark problem. The significant gain in robustness, scalability and efficiency by HFC, with little additional computing effort, and its tolerance of small population sizes, demonstrates its effectiveness on these problems and shows promise of its potential for improving other existing EAs for difficult problems. A paradigm shift from that of most EAs is proposed: rather than trying to escape from local optima or delay convergence at a local optimum, HFC allows the emergence of new optima continually in a bottom-up manner, maintaining low local selection pressure at all fitness levels, while fostering exploitation of high-fitness individuals through promotion to higher levels.
29
Gouvêa, Maury Meirelles, and Aluizio F. R. Araújo. "Evolutionary Algorithm with Diversity-Reference Adaptive Control in Dynamic Environments." International Journal on Artificial Intelligence Tools 24, no. 01 (February 2015): 1450013. http://dx.doi.org/10.1142/s0218213014500134.
Full textAbstract:
Evolutionary algorithms (EAs) can be used to find solutions in dynamic environments. In such cases, after a change in the environment, EAs can either be restarted or they can take advantage of previous knowledge to resume the evolutionary process. The second option tends to be faster and demands less computational effort. The preservation or growth of population diversity is one of the strategies used to advance the evolutionary process after modifications to the environment. We propose a new adaptive method to control population diversity based on a model-reference. The EA evolves the population whereas a control strategy, independently, handles the population diversity. Thus, the adaptive EA evolves a population that follows a diversity-reference model. The proposed model, called the Diversity-Reference Adaptive Control Evolutionary Algorithm (DRAC), aims to maintain or increase the population diversity, thus avoiding premature convergence, and assuring exploration of the solution space during the whole evolutionary process. We also propose a diversity models based on the dynamics of heterozygosity of the population, as models to be tracked by the diversity control. The performance of DRAC showed promising results when compared with the standard genetic algorithm and six other adaptive evolutionary algorithms in 14 different experiments with three different types of environments.
30
Hedar, Abdel-Rahman, Wael Deabes, Majid Almaraashi, and Hesham H. Amin. "Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization." Mathematical and Computational Applications 25, no. 1 (January 16, 2020): 7. http://dx.doi.org/10.3390/mca25010007.
Full textAbstract:
Enhancing Evolutionary Algorithms (EAs) using mathematical elements significantly contribute to their development and control the randomness they are experiencing. Moreover, the automation of the primary process steps of EAs is still one of the hardest problems. Specifically, EAs still have no robust automatic termination criteria. Moreover, the highly random behavior of some evolutionary operations should be controlled, and the methods should invoke advanced learning process and elements. As follows, this research focuses on the problem of automating and controlling the search process of EAs by using sensing and mathematical mechanisms. These mechanisms can provide the search process with the needed memories and conditions to adapt to the diversification and intensification opportunities. Moreover, a new quadratic coding and quadratic search operator are invoked to increase the local search improving possibilities. The suggested quadratic search operator uses both regression and Radial Basis Function (RBF) neural network models. Two evolutionary-based methods are proposed to evaluate the performance of the suggested enhancing elements using genetic algorithms and evolution strategies. Results show that for both the regression, RBFs and quadratic techniques could help in the approximation of high-dimensional functions with the use of a few adjustable parameters for each type of function. Moreover, the automatic termination criteria could allow the search process to stop appropriately.
31
Sun, Liling, Yuhan Wu, Xiaodan Liang, Maowei He, and Hanning Chen. "Constraint Consensus Based Artificial Bee Colony Algorithm for Constrained Optimization Problems." Discrete Dynamics in Nature and Society 2019 (December 25, 2019): 1–24. http://dx.doi.org/10.1155/2019/6523435.
Full textAbstract:
Over the last few decades, evolutionary algorithms (EAs) have been widely adopted to solve complex optimization problems. However, EAs are powerless to challenge the constrained optimization problems (COPs) because they do not directly act to reduce constraint violations of constrained problems. In this paper, the robustly global optimization advantage of artificial bee colony (ABC) algorithm and the stably minor calculation characteristic of constraint consensus (CC) strategy for COPs are integrated into a novel hybrid heuristic algorithm, named ABCCC. CC strategy is fairly effective to rapidly reduce the constraint violations during the evolutionary search process. The performance of the proposed ABCCC is verified by a set of constrained benchmark problems comparing with two state-of-the-art CC-based EAs, including particle swarm optimization based on CC (PSOCC) and differential evolution based on CC (DECC). Experimental results demonstrate the promising performance of the proposed algorithm, in terms of both optimization quality and convergence speed.
32
Muravyov, Sergey, Denis Antipov, Arina Buzdalova, and Andrey Filchenkov. "Efficient Computation of Fitness Function for Evolutionary Clustering." MENDEL 25, no. 1 (June 24, 2019): 87–94. http://dx.doi.org/10.13164/mendel.2019.1.087.
Full textAbstract:
Evolutionary algorithms (EAs) are random search heuristics which can solve various optimization problems. There are plenty of papers describing different approaches developed to apply evolutionary algorithms to the clustering problem, although none of them addressed the problem of fitness function computation. In clustering, many clustering validity indices exist that are designed to evaluate quality of resulting points partition. It is hard to use them as a fitness function due to their computational complexity. In this paper, we propose an efficient method for iterative computation of clustering validity indices which makes application of the EAs to this problem much more appropriate than it was before.
33
Scott, Eric O. "Enhancing Evolutionary Algorithm Performance with Knowledge Transfer and Asynchronous Parallelism." ACM SIGEVOlution 16, no. 1 (March 2023): 1–3. http://dx.doi.org/10.1145/3594261.3594263.
Full textAbstract:
This two-part PhD thesis (by Eric O. Scott, Advisors: Kenneth A. De Jong and Sean Luke) investigates asynchronous steady-state evolutionary algorithms and evolutionary knowledge transfer, respectively, as ways to address challenges that can make it difficult to apply evolutionary algorithms (EAs) to computationally intensive applications. A full copy of the dissertation can be found via ProQuest.
34
Luong, Ngoc Hoang, Han La Poutré, and Peter A. N. Bosman. "Exploiting Linkage Information and Problem-Specific Knowledge in Evolutionary Distribution Network Expansion Planning." Evolutionary Computation 26, no. 3 (September 2018): 471–505. http://dx.doi.org/10.1162/evco_a_00209.
Full textAbstract:
This article tackles the Distribution Network Expansion Planning (DNEP) problem that has to be solved by distribution network operators to decide which, where, and/or when enhancements to electricity networks should be introduced to satisfy the future power demands. Because of many real-world details involved, the structure of the problem is not exploited easily using mathematical programming techniques, for which reason we consider solving this problem with evolutionary algorithms (EAs). We compare three types of EAs for optimizing expansion plans: the classic genetic algorithm (GA), the estimation-of-distribution algorithm (EDA), and the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA). Not fully knowing the structure of the problem, we study the effect of linkage learning through the use of three linkage models: univariate, marginal product, and linkage tree. We furthermore experiment with the impact of incorporating different levels of problem-specific knowledge in the variation operators. Experiments show that the use of problem-specific variation operators is far more important for the classic GA to find high-quality solutions. In all EAs, the marginal product model and its linkage learning procedure have difficulty in capturing and exploiting the DNEP problem structure. GOMEA, especially when combined with the linkage tree structure, is found to have the most robust performance by far, even when an out-of-the-box variant is used that does not exploit problem-specific knowledge. Based on experiments, we suggest that when selecting optimization algorithms for power system expansion planning problems, EAs that have the ability to effectively model and efficiently exploit problem structures, such as GOMEA, should be given priority, especially in the case of black-box or grey-box optimization.
35
Črepinšek, Matej, Shih-Hsi Liu, Marjan Mernik, and Miha Ravber. "Long Term Memory Assistance for Evolutionary Algorithms." Mathematics 7, no. 11 (November 18, 2019): 1129. http://dx.doi.org/10.3390/math7111129.
Full textAbstract:
Short term memory that records the current population has been an inherent component of Evolutionary Algorithms (EAs). As hardware technologies advance currently, inexpensive memory with massive capacities could become a performance boost to EAs. This paper introduces a Long Term Memory Assistance (LTMA) that records the entire search history of an evolutionary process. With LTMA, individuals already visited (i.e., duplicate solutions) do not need to be re-evaluated, and thus, resources originally designated to fitness evaluations could be reallocated to continue search space exploration or exploitation. Three sets of experiments were conducted to prove the superiority of LTMA. In the first experiment, it was shown that LTMA recorded at least 50 % more duplicate individuals than a short term memory. In the second experiment, ABC and jDElscop were applied to the CEC-2015 benchmark functions. By avoiding fitness re-evaluation, LTMA improved execution time of the most time consuming problems F 03 and F 05 between 7% and 28% and 7% and 16%, respectively. In the third experiment, a hard real-world problem for determining soil models’ parameters, LTMA improved execution time between 26% and 69%. Finally, LTMA was implemented under a generalized and extendable open source system, called EARS. Any EA researcher could apply LTMA to a variety of optimization problems and evolutionary algorithms, either existing or new ones, in a uniform way.
36
Shabash, Boris, and Kay C. Wiese. "Diploidy in evolutionary algorithms for dynamic optimization problems." International Journal of Intelligent Computing and Cybernetics 8, no. 4 (November 9, 2015): 312–29. http://dx.doi.org/10.1108/ijicc-07-2015-0026.
Full textAbstract:
Purpose – In this work, the authors show the performance of the proposed diploid scheme (a representation where each individual contains two genotypes) with respect to two dynamic optimization problems, while addressing drawbacks the authors have identified in previous works which compare diploid evolutionary algorithms (EAs) to standard EAs. The paper aims to discuss this issue. Design/methodology/approach – In the proposed diploid representation of EA, each individual possesses two copies of the genotype. In order to convert this pair of genotypes to a single phenotype, each genotype is individually evaluated in relation to the fitness function and the best genotype is presented as the phenotype. In order to provide a fair and objective comparison, the authors make sure to compare populations which contain the same amount of genetic information, where the only difference is the arrangement and interpretation of the information. The two representations are compared using two shifting fitness functions which change at regular intervals to displace the global optimum to a new position. Findings – For small fitness landscapes the haploid (standard) and diploid algorithms perform comparably and are able to find the global optimum very quickly. However, as the search space increases, rediscovering the global optimum becomes more difficult and the diploid algorithm outperforms the haploid algorithm with respect to how fast it relocates the new optimum. Since both algorithms use the same amount of genetic information, it is only fair to conclude it is the unique arrangement of the diploid algorithm that allows it to explore the search space better. Originality/value – The diploid representation presented here is novel in that instead of adopting a dominance scheme for each allele (value) in the vector of values that is the genotype, dominance is adopted across the entire genotype in relation to its homologue. As a result, this representation can be extended across any alphabet, for any optimization function.
37
Alden, Matthew E., Daniel M. Bryan, Brenton J. Lessley, and Arindam Tripathy. "Detection of Financial Statement Fraud Using Evolutionary Algorithms." Journal of Emerging Technologies in Accounting 9, no. 1 (December 1, 2012): 71–94. http://dx.doi.org/10.2308/jeta-50390.
Full textAbstract:
ABSTRACT In this paper, we use a Genetic Algorithm (GA) and MARLEDA—a modern Estimation of Distribution Algorithm (EDA)—to evolve and train several fuzzy rule-based classifiers (FRBCs) to detect patterns of financial statement fraud. We find that both GA and MARLEDA demonstrate a better ability to classify unseen corporate data observations than those of a traditional logistic regression model, and provide validity for detecting financial statement fraud with Evolutionary Algorithms (EAs) and FRBCs. Using ten-fold cross-validation, the GA and MARLEDA yield average training classification accuracy rates of 75.47 percent and 74.26 percent, respectively, and average validation accuracy rates of 63.75 percent and 64.46 percent, respectively.
38
Zhang, Biaobiao, Yue Wu, Jiabin Lu, and K. L. Du. "Evolutionary Computation and Its Applications in Neural and Fuzzy Systems." Applied Computational Intelligence and Soft Computing 2011 (2011): 1–20. http://dx.doi.org/10.1155/2011/938240.
Full textAbstract:
Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.
39
Lu, Tianhao, Chao Bian, and Chao Qian. "Towards Running Time Analysis of Interactive Multi-Objective Evolutionary Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 18 (March 24, 2024): 20777–85. http://dx.doi.org/10.1609/aaai.v38i18.30066.
Full textAbstract:
Evolutionary algorithms (EAs) are widely used for multi-objective optimization due to their population-based nature. Traditional multi-objective EAs (MOEAs) generate a large set of solutions to approximate the Pareto front, leaving a decision maker (DM) with the task of selecting a preferred solution. However, this process can be inefficient and time-consuming, especially when there are many objectives or the DM has subjective preferences. To address this issue, interactive MOEAs (iMOEAs) combine decision making into the optimization process, i.e., update the population with the help of the DM. In contrast to their wide applications, there has existed only two pieces of theoretical works on iMOEAs, which only considered interactive variants of the two simple single-objective algorithms, RLS and (1+1)-EA. This paper provides the first running time analysis (the essential theoretical aspect of EAs) for practical iMOEAs. Specifically, we prove that the expected running time of the well-developed interactive NSGA-II (called R-NSGA-II) for solving the OneMinMax, OneJumpZeroJump problems are all asymptotically faster than the traditional NSGA-II. Meanwhile, we present a variant of OneMinMax, and prove that R-NSGA-II can be exponentially slower than NSGA-II. These results provide theoretical justification for the effectiveness of iMOEAs while identifying situations where they may fail. Experiments are also conducted to validate the theoretical results.
40
Ding, Man, Wei Sun, and Hanning Chen. "Multi-Working Modes Product-Color Planning Based on Evolutionary Algorithms and Swarm Intelligence." Mathematical Problems in Engineering 2010 (2010): 1–15. http://dx.doi.org/10.1155/2010/871301.
Full textAbstract:
In order to assist designer in color planning during product development, a novel synthesized evaluation method is presented to evaluate color-combination schemes of multi-working modes products (MMPs). The proposed evaluation method considers color-combination images in different working modes as evaluating attributes, to which the corresponding weights are assigned for synthesized evaluation. Then a mathematical model is developed to search for optimal color-combination schemes of MMP based on the proposed evaluation method and two powerful search techniques known as Evolution Algorithms (EAs) and Swarm Intelligence (SI). In the experiments, we present a comparative study for two EAs, namely, Genetic Algorithm (GA) and Difference Evolution (DE), and one SI algorithm, namely, Particle Swarm Optimization (PSO), on searching for color-combination schemes of MMP problem. All of the algorithms are evaluated against a test scenario, namely, an Arm-type aerial work platform, which has two working modes. The results show that the DE obtains the superior solution than the other two algorithms for color-combination scheme searching problem in terms of optimization accuracy and computation robustness. Simulation results demonstrate that the proposed method is feasible and efficient.
41
Zhang, Yuhui, Wenhong Wei, Tiezhu Zhao, and Zijia Wang. "Differential Evolution with a Level-Based Learning Strategy for Multimodal Optimization." International Journal of Intelligent Systems 2023 (October 30, 2023): 1–25. http://dx.doi.org/10.1155/2023/3961336.
Full textAbstract:
Multimodal optimization aims at efficiently finding multiple optimal solutions of a problem. Owing to the population-based search mechanism, evolutionary algorithms (EAs) are becoming increasingly popular in solving multimodal optimization problems (MOPs). Most existing work focuses on designing and incorporating niching techniques into EAs so that multiple subpopulations can be formed and assigned to locate different optima. To further enhance the exploration and exploitation abilities of existing EAs, this paper developed a multimodal level-based learning strategy. The basic idea is that individuals should be treated differently according to their positions in the subpopulation. In the evolutionary process, a subpopulation is formed for each candidate solution by grouping its neighboring solutions. Then, individuals in the subpopulation are sorted according to their fitness. Subsequently, the multimodal level-based learning strategy applies different mutation operators to different individuals according to their rankings. Experiments are conducted on a set of benchmark problems to verify the efficacy of the multimodal level-based learning strategy. The results show that the proposed learning strategy can significantly enhance the performance of the existing algorithm. In addition, the algorithm integrated with the proposed strategy is applied to the task of finding multiple roots of nonlinear equation systems (NESs). The results indicate that with the support of the proposed learning strategy, the integrated algorithm compares favorably with state-of-the-art root finding algorithms.
42
ALBA, E., and F. CHICANO. "ON THE BEHAVIOR OF PARALLEL GENETIC ALGORITHMS FOR OPTIMAL PLACEMENT OF ANTENNAE IN TELECOMMUNICATIONS." International Journal of Foundations of Computer Science 16, no. 02 (April 2005): 343–59. http://dx.doi.org/10.1142/s0129054105003029.
Full textAbstract:
In this article, evolutionary algorithms (EAs) are applied to solve the radio network design problem (RND). The task is to find the best set of transmitter locations in order to cover a given geographical region at an optimal cost. Usually, parallel EAs are needed to cope with the high computational requirements of such a problem. Here, we develop and evaluate a set of sequential and parallel genetic algorithms (GAs) to solve the RND problem efficiently. The results show that our distributed steady state GA is an efficient and accurate tool for solving RND that even outperforms existing parallel solutions. The sequential algorithm performs very efficiently from a numerical point of view, although the distributed version is much faster.
43
Moosavian, Naser, and Barbara Lence. "Testing evolutionary algorithms for optimization of water distribution networks." Canadian Journal of Civil Engineering 46, no. 5 (May 2019): 391–402. http://dx.doi.org/10.1139/cjce-2018-0137.
Full textAbstract:
Water distribution networks (WDNs) are one of the most important elements of urban infrastructure and require large investment for construction. Design of WDNs is classified as a large combinatorial discrete nonlinear optimization problem. The main concerns associated with the optimization of such networks are the nonlinearity of the discharge-head loss relationships for pipes and the discrete nature of pipe sizes. Due to these issues, this problem is widely considered to be a benchmark problem for testing and evaluating the performance of nonlinear and heuristic optimization algorithms. This paper compares different techniques, all based on evolutionary algorithms (EAs), which yield optimal solutions for least-cost design of WDNs. All of these algorithms search for the global optimum starting from populations of solutions, rather than from a single solution, as in Newton-based search methods. They use different operators to improve the performance of many solutions over repeated iterations. Ten EAs, four of them for the first time, are applied to the design of three networks and their performance in terms of the least cost, under different stopping criteria, are evaluated. Statistical information for 20 executions of the ten algorithms is summarized, and Friedman tests are conducted. Results show that, for the two-loop benchmark network, the particle swarm optimization gravitational search and biology and bioinformatics global optimization algorithms efficiently converge to the global optimum, but perform poorly for large networks. In contrast, given a sufficient number of function evaluations, the covariance matrix adaptation evolution strategy and soccer league competition algorithm consistently converge to the global optimum, for large networks.
44
SRINIVAS, G., A. K. VERMA, A. SRIVIDYA, and SANJAY KUMAR KHATTRI. "RELIABILITY BASED OPTIMIZATION OF TECHNICAL SPECIFICATION OF FRONTLINE SYSTEMS OF NUCLEAR POWER PLANTS USING MULTI-OBJECTIVE APPROACH." International Journal of Reliability, Quality and Safety Engineering 19, no. 01 (February 2012): 1250002. http://dx.doi.org/10.1142/s0218539312500027.
Full textAbstract:
Technical Specifications define the limiting conditions of operation, maintenance and surveillance test requirements for the various Nuclear Power plant systems in order to meet the safety requirements to fulfill regulatory criteria. These specifications impact even the economics of the plant. The regulatory approach addresses only the safety criteria, while the plant operators would like to balance the cost criteria too. The attempt to optimize both the conflicting requirements presents a case to use Multi-objective optimization. Evolutionary algorithms (EAs) mimic natural evolutionary principles to constitute search and optimization procedures. Genetic algorithms are a particular class of EA's that use techniques inspired by evolutionary biology such as inheritance, mutation, natural selection and recombination (or cross-over). In this paper we have used the plant insights obtained through a detailed Probabilistic Safety Assessment with the Genetic Algorithm approach for Multi-objective optimization of Surveillance test intervals. The optimization of Technical Specifications of three front line systems is performed using the Genetic Algorithm Approach. The selection of these systems is based on their importance to the mitigation of possible accident sequences which are significant to potential core damage of the nuclear power plant.
45
Wang, Lei, Qian Sun, Qingzheng Xu, Wei Li, and Qiaoyong Jiang. "Analysis of Multitasking Evolutionary Algorithms under the Order of Solution Variables." Complexity 2020 (October 14, 2020): 1–18. http://dx.doi.org/10.1155/2020/4609489.
Full textAbstract:
Recently, it was demonstrated that multitasking evolutionary algorithm (MTEA), a newly proposed algorithm, can solve multiple optimization problems simultaneously through a single run, breaking through the limitations of traditional evolutionary algorithms (EAs), with good convergence and exploration performance. As a novel algorithm, MTEA still has a lot of unexplored space. Generally speaking, the order of solution variables has no significant influence on the single-tasking EAs. To our knowledge, the effect of the order of variables in the multitasking scenario has not been explored. To fill in this research gap, three orders of variables in the multitasking scenario are proposed in this paper, including full reverse order, bisection reverse order, and trisection reverse order. An important feature of these orders of variables is that an individual can recover as himself after two times of changing the order of variables. In order to verify our idea, these orders of variables are embedded into MTEA. The experiment results revealed that the effect of the different orders of variables is universal but not significant enough in the practical application. Furthermore, tasks with high similarity and high degree of intersection are sensitive to the order of variables and get great impact between tasks.
46
Ugolotti, Roberto, Laura Sani, and Stefano Cagnoni. "What Can We Learn from Multi-Objective Meta-Optimization of Evolutionary Algorithms in Continuous Domains?" Mathematics 7, no. 3 (March 4, 2019): 232. http://dx.doi.org/10.3390/math7030232.
Full textAbstract:
Properly configuring Evolutionary Algorithms (EAs) is a challenging task made difficult by many different details that affect EAs’ performance, such as the properties of the fitness function, time and computational constraints, and many others. EAs’ meta-optimization methods, in which a metaheuristic is used to tune the parameters of another (lower-level) metaheuristic which optimizes a given target function, most often rely on the optimization of a single property of the lower-level method. In this paper, we show that by using a multi-objective genetic algorithm to tune an EA, it is possible not only to find good parameter sets considering more objectives at the same time but also to derive generalizable results which can provide guidelines for designing EA-based applications. In particular, we present a general framework for multi-objective meta-optimization, to show that “going multi-objective” allows one to generate configurations that, besides optimally fitting an EA to a given problem, also perform well on previously unseen ones.
47
Yin, Peng-Yeng, and Hsi-Li Wu. "Cyber-EDA: Estimation of Distribution Algorithms with Adaptive Memory Programming." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/132697.
Full textAbstract:
The estimation of distribution algorithm (EDA) aims to explicitly model the probability distribution of the quality solutions to the underlying problem. By iterative filtering for quality solution from competing ones, the probability model eventually approximates the distribution of global optimum solutions. In contrast to classic evolutionary algorithms (EAs), EDA framework is flexible and is able to handle inter variable dependence, which usually imposes difficulties on classic EAs. The success of EDA relies on effective and efficient building of the probability model. This paper facilitates EDA from the adaptive memory programming (AMP) domain which has developed several improved forms of EAs using the Cyber-EA framework. The experimental result on benchmark TSP instances supports our anticipation that the AMP strategies can enhance the performance of classic EDA by deriving a better approximation for the true distribution of the target solutions.
48
Talpur, Fauzia, Imtiaz Ali Korejo, Aftab Ahmed Chandio, Ali Ghulam, and Mir Sajjad Hussain Talpur. "ML-Based Detection of DDoS Attacks Using Evolutionary Algorithms Optimization." Sensors 24, no. 5 (March 5, 2024): 1672. http://dx.doi.org/10.3390/s24051672.
Full textAbstract:
The escalating reliance of modern society on information and communication technology has rendered it vulnerable to an array of cyber-attacks, with distributed denial-of-service (DDoS) attacks emerging as one of the most prevalent threats. This paper delves into the intricacies of DDoS attacks, which exploit compromised machines numbering in the thousands to disrupt data services and online commercial platforms, resulting in significant downtime and financial losses. Recognizing the gravity of this issue, various detection techniques have been explored, yet the quantity and prior detection of DDoS attacks has seen a decline in recent methods. This research introduces an innovative approach by integrating evolutionary optimization algorithms and machine learning techniques. Specifically, the study proposes XGB-GA Optimization, RF-GA Optimization, and SVM-GA Optimization methods, employing Evolutionary Algorithms (EAs) Optimization with Tree-based Pipelines Optimization Tool (TPOT)-Genetic Programming. Datasets pertaining to DDoS attacks were utilized to train machine learning models based on XGB, RF, and SVM algorithms, and 10-fold cross-validation was employed. The models were further optimized using EAs, achieving remarkable accuracy scores: 99.99% with the XGB-GA method, 99.50% with RF-GA, and 99.99% with SVM-GA. Furthermore, the study employed TPOT to identify the optimal algorithm for constructing a machine learning model, with the genetic algorithm pinpointing XGB-GA as the most effective choice. This research significantly advances the field of DDoS attack detection by presenting a robust and accurate methodology, thereby enhancing the cybersecurity landscape and fortifying digital infrastructures against these pervasive threats.
49
Shen, Yong, Yunlou Zhu, Hongwei Kang, Xingping Sun, Qingyi Chen, and Da Wang. "UAV Path Planning Based on Multi-Stage Constraint Optimization." Drones 5, no. 4 (December 6, 2021): 144. http://dx.doi.org/10.3390/drones5040144.
Full textAbstract:
Evolutionary Algorithms (EAs) based Unmanned Aerial Vehicle (UAV) path planners have been extensively studied for their effectiveness and high concurrency. However, when there are many obstacles, the path can easily violate constraints during the evolutionary process. Even if a single waypoint causes a few constraint violations, the algorithm will discard these solutions. In this paper, path planning is constructed as a multi-objective optimization problem with constraints in a three-dimensional terrain scenario. To solve this problem in an effective way, this paper proposes an evolutionary algorithm based on multi-level constraint processing (ANSGA-III-PPS) to plan the shortest collision-free flight path of a gliding UAV. The proposed algorithm uses an adaptive constraint processing mechanism to improve different path constraints in a three-dimensional environment and uses an improved adaptive non-dominated sorting genetic algorithm (third edition—ANSGA-III) to enhance the algorithm’s path planning ability in a complex environment. The experimental results show that compared with the other four algorithms, ANSGA-III-PPS achieves the best solution performance. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of UAV path planning.
50
Wang, Mingzhao, Yuping Wang, and Xiaoli Wang. "A Space Division Multiobjective Evolutionary Algorithm Based on Adaptive Multiple Fitness Functions." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 03 (February 22, 2016): 1659005. http://dx.doi.org/10.1142/s0218001416590059.
Full textAbstract:
The weighted sum of objective functions is one of the simplest fitness functions widely applied in evolutionary algorithms (EAs) for multiobjective programming. However, EAs with this fitness function cannot find uniformly distributed solutions on the entire Pareto front for nonconvex and complex multiobjective programming. In this paper, a novel EA based on adaptive multiple fitness functions and adaptive objective space division is proposed to overcome this shortcoming. The objective space is divided into multiple regions of about the same size by uniform design, and one fitness function is defined on each region by the weighted sum of objective functions to search for the nondominated solutions in this region. Once a region contains fewer nondominated solutions, it is divided into several sub-regions and one additional fitness function is defined on each sub-region. The search will be carried out simultaneously in these sub-regions, and it is hopeful to find more nondominated solutions in such a region. As a result, the nondominated solutions in each region are changed adaptively, and eventually are uniformly distributed on the entire Pareto front. Moreover, the complexity of the proposed algorithm is analyzed. The proposed algorithm is applied to solve 13 test problems and its performance is compared with that of 10 widely used algorithms. The results show that the proposed algorithm can effectively handle nonconvex and complex problems, generate widely spread and uniformly distributed solutions on the entire Pareto front, and outperform those compared algorithms.