Relevant bibliographies by topics / Genetic Algorithm Heuristic

Academic literature on the topic 'Genetic Algorithm Heuristic'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Genetic Algorithm Heuristic"

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E. Nugraheni, Cecilia, Luciana Abednego, and Maria Widyarini. "A Combination of Palmer Algorithm and Gupta Algorithm for Scheduling Problem in Apparel Industry." International Journal of Fuzzy Logic Systems 11, no. 1 (January 31, 2021): 1–12. http://dx.doi.org/10.5121/ijfls.2021.11101.

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The apparel industry is a class of textile industry. Generally, the production scheduling problem in the apparel industry belongs to Flow Shop Scheduling Problems (FSSP). There are many algorithms/techniques/heuristics for solving FSSP. Two of them are the Palmer Algorithm and the Gupta Algorithm. Hyper-heuristic is a class of heuristics that enables to combine of some heuristics to produce a new heuristic. GPHH is a hyper-heuristic that is based on genetic programming that is proposed to solve FSSP [1]. This paper presents the development of a computer program that implements the GPHH. Some experiments have been conducted for measuring the performance of GPHH. From the experimental results, GPHH has shown a better performance than the Palmer Algorithm and Gupta Algorithm.
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Lie, Luo. "Heuristic Artificial Intelligent Algorithm for Genetic Algorithm." Key Engineering Materials 439-440 (June 2010): 516–21. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.516.

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A genetic algorithm is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.
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Chen, James C., Chien Wei Wu, Tran Dinh Duy Thao, Ling Huey Su, Wen Haiung Hsieh, and Tiffany Chen. "Hybrid Genetic Algorithm for Solving Assembly Line Balancing Problem in Footwear Industry." Advanced Materials Research 939 (May 2014): 623–29. http://dx.doi.org/10.4028/www.scientific.net/amr.939.623.

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This research develops a heuristic algorithm for assembly line balancing problem (ALBP) of stitching lines in footwear industry. The proposed algorithm can help to design the stitching line with workstations, machines and operators for the production of every new product model. Rank-positional-weighted heuristics and hybrid genetic algorithms are proposed to solve ALBP. First, the heuristics assign tasks and machines to workstations. This solution is then used as an initiative population for hybrid genetic algorithm for further improvement. Real data from footwear manufacturers and experimental designs are used to verify the performance of the proposed algorithm, comparing with one existing bidirectional heuristic. Results indicate that when the size and shape of shoes increase, the proposed genetic algorithm achieves better solution quality than existing heuristics.Production managers can use the research results to quickly design stitching lines for short production cycle time and high labor utilization.
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Nurserik, D., F. R. Gusmanova, G. А. Abdulkarimova, and K. S. Dalbekova. "OVERVIEW OF HEURISTIC AND METAHEURISTIC ALGORITHMS." BULLETIN Series of Physics & Mathematical Sciences 71, no. 3 (September 30, 2020): 242–47. http://dx.doi.org/10.51889/2020-3.1728-7901.37.

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The article discusses the use of heuristic algorithms for optimization problems. The algorithms for stochastic optimization are described, which constitute the main properties of the metaheuristic and its classes. Evolutionary algorithms are described in general terms. In particular, the main steps and properties of genetic algorithms are presented. The main goal of this article is to solve the vehicle routing problem using a metaheuristic algorithm. The vehicle routing problem is a complex combinatorial NP-complete optimization problem. It is shown that the metaheuristic approach to solving the problem allows one to obtain a suboptimal solution without examining the entire space of possible solutions. The genetic algorithm belongs to the group of evolutionary algorithms. The definitions are briefly given to the terms characteristic of the genetic algorithm: gene, chromosome, personality (descendant), population, descendant operators, crossing, mutation, crossover. Application of the theory of finite automata in a genetic algorithm is described. The terminology and scheme of the genetic algorithm for solving various problems are proposed.
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Kaweegitbundit, Parinya. "Comparison of Heuristic for Flow Shop Scheduling Problems with Sequence Dependent Setup Time." Advanced Materials Research 339 (September 2011): 332–35. http://dx.doi.org/10.4028/www.scientific.net/amr.339.332.

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This paper considers flow shop scheduling problems with sequence dependent setup time. The makespan criterion has been considered. In this paper presented a comparison of three heuristics for solves this problem. The memetic algorithm, genetic algorithm and NEH heuristic have been compared. In the experimental, the result from memetic algorithm is maximum the best solution. Therefore, the MA heuristic outperforms other heuristic.
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Ardelean, Sebastian Mihai, and Mihai Udrescu. "Graph coloring using the reduced quantum genetic algorithm." PeerJ Computer Science 7 (January 3, 2022): e836. http://dx.doi.org/10.7717/peerj-cs.836.

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Genetic algorithms (GA) are computational methods for solving optimization problems inspired by natural selection. Because we can simulate the quantum circuits that implement GA in different highly configurable noise models and even run GA on actual quantum computers, we can analyze this class of heuristic methods in the quantum context for NP-hard problems. This paper proposes an instantiation of the Reduced Quantum Genetic Algorithm (RQGA) that solves the NP-hard graph coloring problem in O(N1/2). The proposed implementation solves both vertex and edge coloring and can also determine the chromatic number (i.e., the minimum number of colors required to color the graph). We examine the results, analyze the algorithm convergence, and measure the algorithm's performance using the Qiskit simulation environment. Our Reduced Quantum Genetic Algorithm (RQGA) circuit implementation and the graph coloring results show that quantum heuristics can tackle complex computational problems more efficiently than their conventional counterparts.
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Tong, Zhao, Hongjian Chen, Bilan Liu, Jinhui Cai, and Shuo Cai. "A novel intelligent hyper-heuristic algorithm for solving optimization problems." Journal of Intelligent & Fuzzy Systems 42, no. 6 (April 28, 2022): 5041–53. http://dx.doi.org/10.3233/jifs-211250.

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In recent years, solving combinatorial optimization problems involves more complications, high dimensions, and multi-objective considerations. Combining the advantages of other evolutionary algorithms to enhance the performance of a unique evolutionary algorithm and form a new hybrid heuristic algorithm has become a way to strengthen the performance of the algorithm effectively. However, the intelligent hybrid heuristic algorithm destroys the integrity, universality, and robustness of the original algorithm to a certain extent and increases its time complexity. This paper implements a new idea “ML to choose heuristics” (a heuristic algorithm combined with machine learning technology) which uses the Q-learning method to learn different strategies in genetic algorithm. Moreover, a selection-based hyper-heuristic algorithm is obtained that can guide the algorithm to make decisions at different time nodes to select appropriate strategies. The algorithm is the hybrid strategy using Q-learning on StudGA (HSQ-StudGA). The experimental results show that among the 14 standard test functions, the evolutionary algorithm guided by Q-learning can effectively improve the quality of arithmetic solution. Under the premise of not changing the evolutionary structure of the algorithm, the hyper-heuristic algorithm represents a new method to solve combinatorial optimization problems.
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Tong, Zhao, Hongjian Chen, Bilan Liu, Jinhui Cai, and Shuo Cai. "A novel intelligent hyper-heuristic algorithm for solving optimization problems." Journal of Intelligent & Fuzzy Systems 42, no. 6 (April 28, 2022): 5041–53. http://dx.doi.org/10.3233/jifs-211250.

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In recent years, solving combinatorial optimization problems involves more complications, high dimensions, and multi-objective considerations. Combining the advantages of other evolutionary algorithms to enhance the performance of a unique evolutionary algorithm and form a new hybrid heuristic algorithm has become a way to strengthen the performance of the algorithm effectively. However, the intelligent hybrid heuristic algorithm destroys the integrity, universality, and robustness of the original algorithm to a certain extent and increases its time complexity. This paper implements a new idea “ML to choose heuristics” (a heuristic algorithm combined with machine learning technology) which uses the Q-learning method to learn different strategies in genetic algorithm. Moreover, a selection-based hyper-heuristic algorithm is obtained that can guide the algorithm to make decisions at different time nodes to select appropriate strategies. The algorithm is the hybrid strategy using Q-learning on StudGA (HSQ-StudGA). The experimental results show that among the 14 standard test functions, the evolutionary algorithm guided by Q-learning can effectively improve the quality of arithmetic solution. Under the premise of not changing the evolutionary structure of the algorithm, the hyper-heuristic algorithm represents a new method to solve combinatorial optimization problems.
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Chen, James C., Wun Hao Jaong, Cheng Ju Sun, Hung Yu Lee, Jenn Sheng Wu, and Chung Chao Ku. "Applying Genetic Algorithm to Resource Constrained Multi-Project Scheduling Problems." Key Engineering Materials 419-420 (October 2009): 633–36. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.633.

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Resource-constrained multi-project scheduling problems (RCMPSP) consider precedence relationship among activities and the capacity constraints of multiple resources for multiple projects. RCMPSP are NP-hard due to these practical constraints indicating an exponential calculation time to reach optimal solution. In order to improve the speed and the performance of problem solving, heuristic approaches are widely applied to solve RCMPSP. This research proposes Hybrid Genetic Algorithm (HGA) and heuristic approach to solve RCMPSP with an objective to minimize the total tardiness. HGA is compared with three typical heuristics for RCMPSP: Maximum Total Work Content, Earliest Due Date, and Minimum Slack. Two typical RCMPSP from literature are used as a test bed for performance evaluation. The results demonstrate that HGA outperforms the three heuristic methods in term of the total tardiness.
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Noshadi, Tayebe, Marzieh Dadvar, Nastaran Mirza, and Shima Shamseddini. "Adjust genetic algorithm parameter by fuzzy system." Ciência e Natura 37 (December 19, 2015): 190. http://dx.doi.org/10.5902/2179460x20771.

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Genetic algorithm is one of the random searches algorithm. Genetic algorithm is a method that uses genetic evolution as a model of problem solving. Genetic algorithm for selecting the best population, but the choices are not as heuristic information to be used in specific issues. In order to obtain optimal solutions and efficient use of fuzzy systems with heuristic rules that we would aim to increase the efficiency of parallel genetic algorithms using fuzzy logic immigration, which in fact do this by optimizing the parameters compared with the use of fuzzy system is done.
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Dissertations / Theses on the topic "Genetic Algorithm Heuristic"

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Komínek, Jan. "Heuristické algoritmy pro optimalizaci." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2012. http://www.nusl.cz/ntk/nusl-230306.

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This diploma thesis deals with genetic algorithms and their properties. Particular emphasis is placed on finding the influence of mutation and population size. Genetic algorithms are applied on inverse heat conduction problems (IHCP) in the second part of the thesis. Several different approaches and coding methods were tested. Properties of genetic algorithms were improved by definition of two new genetic operators – manipulation and sorting. Reported theoretical findings were tested on the real data of inverse heat conduction problem. The library for easy implementation of GA for solving general optimization problems in C ++ was created and is described in the last chapter.
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Bilal, Mohd. "A Heuristic Search Algorithm for Asteroid Tour Missions." Thesis, Luleå tekniska universitet, Rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71361.

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Since the discovery of Ceres, asteroids have been of immense scientific interest and intrigue. They hold answers to many of the fundamental questionsabout the formation and evolution of the Solar System. Therefore, a missionsurveying the asteroid belt with close encounter of carefully chosen asteroidswould be of immense scientific benefit. The trajectory of such an asteroidtour mission needs to be designed such that asteroids of a wide range ofcompositions and sizes are encountered; all with an extremely limited ∆Vbudget.This thesis presents a novel heuristic algorithm to optimize trajectoriesfor an asteroid tour mission with close range flybys (≤ 1000 km). The coresearch algorithm efficiently decouples combinatorial (i.e. choosing the asteroids to flyby)and continuous optimization (i.e. optimizing critical maneuversand events) of what is essentially a mixed integer programming problem.Additionally, different methods to generate a healthy initial population forthe combinatorial optimization are presented.The algorithm is used to generate a set of 1800 feasible trajectories withina 2029+ launch frame. A statistical analysis of these set of trajectories isperformed and important metrics for the search are set based on the statistics.Trajectories allowing flybys to prominent families of asteroids like Flora andNysa with ∆V as low as 4.99 km/s are obtained.Two modified implementations of the algorithm are presented. In a firstiteration, a large sample of trajectories is generated with a limited numberof encounters to the most scientifically interesting targets. While, a posteriori, trajectories are filled in with as many small targets as possible. Thisis achieved in two different ways, namely single step extension and multiplestep extension. The former fills in the trajectories with small targets in onestep, while the latter optimizes the trajectory by filling in with one asteroid per step. The thesis also presents detection of asteroids for successfullyperforming flybys. A photometric filter is developed which prunes out badlyilluminated asteroids. The best trajectory is found to perform well againstthis filter such that nine out of the ten planned flybys are feasible.
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Ma, Jiya. "A Genetic Algorithm for Solar Boat." Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3488.

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Genetic algorithm has been widely used in different areas of optimization problems. Ithas been combined with renewable energy domain, photovoltaic system, in this thesis.To participate and win the solar boat race, a control program is needed and C++ hasbeen chosen for programming. To implement the program, the mathematic model hasbeen built. Besides, the approaches to calculate the boundaries related to conditionhave been explained. Afterward, the processing of the prediction and real time controlfunction are offered. The program has been simulated and the results proved thatgenetic algorithm is helpful to get the good results but it does not improve the resultstoo much since the particularity of the solar driven boat project such as the limitationof energy production
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Lianjie, Shen. "Optimization and Search in Model-Based Automotive SW/HW Development." Thesis, Linköpings universitet, Programvara och system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-105394.

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In this thesis two case studies are performed about solving two design problems we face during the design phase of new Volvo truck. One is to solve the frame packing problem on CAN bus. The other is to solve the LDC allocation problem. Both solutions are targeted to meet as many end-to-end latency requirements as possible. Now the solution is obtained through manually approach and based on the designer experience. But it is still not satisfactory enough. With the development of artificial intelligence method we propose two methods based on genetic algorithm to solve our design problem we face today. In first case study about frame packing we perform one single genetic algorithm process to find the optimal solution. In second case study about LDC allocation we proposed how to handle two genetic algorithm processes together to reach the optimal solution. In this thesis we show the feasibility of adopting artificial intelligence concept in some activities of the truck design phases like we do in both case studies.
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Han, Limin. "An investigation of a genetic algorithm based hyper-heuristic applied to scheduling problems." Thesis, University of Nottingham, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.422736.

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Cheng, Lin. "A genetic algorithm for the vehicle routing problem with time windows /." Electronic version (PDF), 2005. http://dl.uncw.edu/etd/2005/chengl/lincheng.pdf.

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Woodside-Oriakhi, Maria. "Portfolio optimisation with transaction cost." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5839.

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Portfolio selection is an example of decision making under conditions of uncertainty. In the face of an unknown future, fund managers make complex financial choices based on the investors perceptions and preferences towards risk and return. Since the seminal work of Markowitz, many studies have been published using his mean-variance (MV) model as a basis. These mathematical models of investor attitudes and asset return dynamics aid in the portfolio selection process. In this thesis we extend the MV model to include the cardinality constraints which limit the number of assets held in the portfolio and bounds on the proportion of an asset held (if any is held). We present our formulation based on the Markowitz MV model for rebalancing an existing portfolio subject to both fixed and variable transaction cost (the fee associated with trading). We determine and demonstrate the differences that arise in the shape of the trading portfolio and efficient frontiers when subject to non-cardinality and cardinality constrained transaction cost models. We apply our flexible heuristic algorithms of genetic algorithm, tabu search and simulated annealing to both the cardinality constrained and transaction cost models to solve problems using data from seven real world market indices. We show that by incorporating optimization into the generation of valid portfolios leads to good quality solutions in acceptable computational time. We illustrate this on problems from literature as well as on our own larger data sets.
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Hanek, Petr. "Implementace problému směrování vozidel pomocí algoritmu mravenčích kolonií a částicových rojů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400931.

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This diploma thesis focuses on meta-heuristic algorithms and their ability to solve difficult optimization problems in polynomial time. The thesis describes different kinds of meta-heuristic algorithms such as genetic algorithm, particle swarm optimization or ant colony optimization. The implemented application was written in Java and contains ant colony optimization for capacitated vehicle routing problem and particle swarm optimization which finds the best possible parameters for ant colonies.
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Demirbas, Korkut. "Optimal Management Of Coastal Aquifers Using Heuristic Algorithms." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613135/index.pdf.

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Excessive pumping in coastal aquifers results in seawater intrusion where optimal and efficient planning is essential. In this study, numerical solution of single potential solution by Strack is combined with genetic algorithm (GA) to find the maximum extraction amount in a coastal aquifer. Seawater intrusion is tracked with the potential value at the extraction well locations. A code is developed by combining GA and a subroutine repeatedly calling MODFLOW as a numerical solver to calculate the potential distribution for different configurations of solution (trial solutions). Potential distributions are used to evaluate the fitness values for GA. The developed model is applied to a previous work by Mantoglou. Another heuristic method, simulated annealing (SA) is utilized to compare the results of GA. Different seawater prevention methods (i.e. injection wells, canals) and decision variables related to those methods (i.e. location of the injection wells or canals) are added to model to further prevent the seawater intrusion and improve the coastal aquifer benefit. A method called &ldquo
Alternating Constraints Method&rdquo
is introduced to improve the solution for the cases with variable location. The results show that both proposed method and the regular solution with GA or SA prove to be successful methods for the optimal management of coastal aquifers.
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Hassan, Fadratul Hafinaz. "Heuristic search methods and cellular automata modelling for layout design." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/7581.

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Spatial layout design must consider not only ease of movement for pedestrians under normal conditions, but also their safety in panic situations, such as an emergency evacuation in a theatre, stadium or hospital. Using pedestrian simulation statistics, the movement of crowds can be used to study the consequences of different spatial layouts. Previous works either create an optimal spatial arrangement or an optimal pedestrian circulation. They do not automatically optimise both problems simultaneously. Thus, the idea behind the research in this thesis is to achieve a vital architectural design goal by automatically producing an optimal spatial layout that will enable smooth pedestrian flow. The automated process developed here allows the rapid identification of layouts for large, complex, spatial layout problems. This is achieved by using Cellular Automata (CA) to model pedestrian simulation so that pedestrian flow can be explored at a microscopic level and designing a fitness function for heuristic search that maximises these pedestrian flow statistics in the CA simulation. An analysis of pedestrian flow statistics generated from feasible novel design solutions generated using the heuristic search techniques (hill climbing, simulated annealing and genetic algorithm style operators) is conducted. The statistics that are obtained from the pedestrian simulation is used to measure and analyse pedestrian flow behaviour. The analysis from the statistical results also provides the indication of the quality of the spatial layout design generated. The technique has shown promising results in finding acceptable solutions to this problem when incorporated with the pedestrian simulator when demonstrated on simulated and real-world layouts with real pedestrian data.
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Books on the topic "Genetic Algorithm Heuristic"

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Wagner, Stefan. Looking inside genetic algorithms. Linz: Trauner Verlag, 2004.

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Genetic algorithms and robotics: A heuristic strategy for optimization. Singapore: World Scientific, 1991.

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Carter, Jason W. Testing effectiveness of genetic algorithms for exploratory data analysis. Monterey, Calif: Naval Postgraduate School, 1997.

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Parker, Gary B. Genetic algorithms for the development of real-time multi-heuristic search strategies. Monterey, Calif: Naval Postgraduate School, 1992.

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1965-, Karaboga Dervis, ed. Intelligent optimisation techniques: Genetic algorithms, tabu search, simulated annealing and neural networks. London: Springer, 2000.

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D, Karaboga, ed. Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks. London: Springer London, 2000.

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Vose, Michael D. The Simple Genetic Algorithm. The MIT Press, 1999. http://dx.doi.org/10.7551/mitpress/6229.001.0001.

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The Simple Genetic Algorithm (SGA) is a classical form of genetic search. Viewing the SGA as a mathematical object, Michael D. Vose provides an introduction to what is known (i.e., proven) about the theory of the SGA. He also makes available algorithms for the computation of mathematical objects related to the SGA. Although he describes the SGA in terms of heuristic search, the book is not about search or optimization per se. Rather, the focus is on the SGA as an evolutionary system. The author intends the book also to serve as an outline for exploring topics in mathematics and computer science in a goal-oriented way. Bradford Books imprint
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Wiener, Richard. Generic Data Structures and Algorithms in Go: An Applied Approach Using Concurrency, Genericity and Heuristics. Apress L. P., 2022.

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Book chapters on the topic "Genetic Algorithm Heuristic"

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Munetomo, Masaharu. "The Genetic Adaptive Routing Algorithm." In Telecommunications Optimization: Heuristic and Adaptive Techniques, 151–66. Chichester, UK: John Wiley & Sons, Ltd, 2001. http://dx.doi.org/10.1002/047084163x.ch9.

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Han, Limin, and Graham Kendall. "Guided Operators for a Hyper-Heuristic Genetic Algorithm." In Lecture Notes in Computer Science, 807–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24581-0_69.

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Smith, George D., Justin C. W. Debuse, Mark D. Ryan, and Ian M. Whittley. "An Effective Genetic Algorithm for the Fixed Channel Assignment Problem." In Telecommunications Optimization: Heuristic and Adaptive Techniques, 357–71. Chichester, UK: John Wiley & Sons, Ltd, 2001. http://dx.doi.org/10.1002/047084163x.ch19.

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Armony, Mor, John G. Klincewicz, Hanan Luss, and Moshe B. Rosenwein. "Design of Stacked Self-Healing Rings Using a Genetic Algorithm." In Heuristic Approaches for Telecommunications Network Management, Planning and Expansion, 85–105. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-5392-9_5.

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Auyeung, Andy, Iker Gondra, and H. K. Dai. "Integrating Random Ordering into Multi-heuristic List Scheduling Genetic Algorithm." In Intelligent Systems Design and Applications, 447–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44999-7_43.

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Sevinç, Ender, and Ahmet Coşar. "Distributed Database Design with Genetic Algorithm and Relation Clustering Heuristic." In Lecture Notes in Electrical Engineering, 133–36. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9794-1_27.

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Bonab, Mohammad Babrdel, Goi Bok-Min, Madhavan a/l Balan Nair, Chua Kein Huat, and Wong Chim Chwee. "A New Genetic-Based Hyper-Heuristic Algorithm for Clustering Problem." In Advances in Intelligent Systems and Computing, 145–55. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73689-7_15.

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Puente, Jorge, Camino R. Vela, Carlos Prieto, and Ramiro Varela. "Hybridizing a Genetic Algorithm with Local Search and Heuristic Seeding." In Artificial Neural Nets Problem Solving Methods, 329–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44869-1_42.

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Sinclair, M. C. "NOMaD: Applying a Genetic Algorithm/Heuristic Hybrid Approach to Optical Network Topology Design." In Artificial Neural Nets and Genetic Algorithms, 299–303. Vienna: Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6492-1_65.

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Xiao, Jian-Ping, Xiao-Min Hu, and Wei-Neng Chen. "Dynamic Cloud Workflow Scheduling with a Heuristic-Based Encoding Genetic Algorithm." In Neural Information Processing, 38–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63833-7_4.

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Conference papers on the topic "Genetic Algorithm Heuristic"

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Li, Jiafei, Jihong OuYang, and Mingyong Feng. "A Heuristic Genetic Process Mining Algorithm." In 2011 Seventh International Conference on Computational Intelligence and Security (CIS 2011). IEEE, 2011. http://dx.doi.org/10.1109/cis.2011.12.

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Yiu, Ying Fung, Jing Du, and Rabi Mahapatra. "Evolutionary Heuristic A* Search: Heuristic Function Optimization via Genetic Algorithm." In 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). IEEE, 2018. http://dx.doi.org/10.1109/aike.2018.00012.

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Shi, Hong, and Jin-Zong Fu. "A Heuristic Genetic Algorithm of Attribute Reduction." In Proceedings of 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258670.

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Chakraborty, U. K., D. Lah, and M. Chakraborty. "A heuristic genetic algorithm for flowshop scheduling." In Proceedings 23rd International Conference Information Technology Interfaces. ITI 2001. IEEE, 2001. http://dx.doi.org/10.1109/iti.2001.938035.

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Zhou, Yangming, and Jin-Kao Hao. "A fast heuristic algorithm for the critical node problem." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3067695.3075993.

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Kazakovtsev, Lev A., Mikhail N. Gudyma, and Alexander N. Antamoshkin. "Genetic algorithm with greedy heuristic for capacity planning." In 2014 6th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). IEEE, 2014. http://dx.doi.org/10.1109/icumt.2014.7002170.

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Poulding, Simon, and Robert Feldt. "Heuristic Model Checking using a Monte-Carlo Tree Search Algorithm." In GECCO '15: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2739480.2754767.

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Wang, Dezhi, Jinying Gan, and Deyu Wang. "Heuristic Genetic Algorithm for Multicast Overlay Network Link Selection." In 2008 Second International Conference on Genetic and Evolutionary Computing (WGEC). IEEE, 2008. http://dx.doi.org/10.1109/wgec.2008.44.

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9

Young, Kevin M., and Scott M. Ferguson. "Intelligent Genetic Algorithm Crossover Operators for Market-Driven Design." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59534.

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Abstract:
Heuristic algorithms have been adopted as a means of developing solutions for complex problems within the design community. Previous research has looked into the implications of genetic algorithm tuning when applied to solving product line optimization problems. This study investigates the effects of developing informed heuristic operators for product line optimization problems, specifically in regards to optimizing the market share of preference of an automobile product line. Informed crossover operators constitute operators that use problem-related information to inform their actions within the algorithm. For this study, a crossover operator that alters its actions based on the relative market share of preference for each product within product lines was found to be most effective. The presented results indicate a significant improvement in computational efficiency and increases in market share of preference when compared to a standard scattered crossover approach. Future work in this subject will investigate the development of additional informed selection and mutation operators, as well as problem informed schema.
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Fatmi, Abdelhakim El, Arakil Chentoufi, M. Ali Bekri, Said Benhlima, and Mohamed Sabbane. "A heuristic algorithm for RNA secondary structure based on genetic algorithm." In 2017 Intelligent Systems and Computer Vision (ISCV). IEEE, 2017. http://dx.doi.org/10.1109/isacv.2017.8054964.

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