Proceedings of the third international conference on Genetic algorithms
Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Forking Genetic Algorithm with Blocking and Shrinking Modes (fGA)
Proceedings of the 5th International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A sequential niche technique for multimodal function optimization
Evolutionary Computation
Changing representations during search: A comparative study of delta coding
Evolutionary Computation
Genetic algorithms as global random search methods: An alternative perspective
Evolutionary Computation
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
Efficiency speed-up strategies for evolutionary computation: fundamentals and fast-GAs
Applied Mathematics and Computation
Designing evolutionary algorithms for dynamic optimization problems
Advances in evolutionary computing
Advances in evolutionary computing
A new adaptive genetic algorithm for fixed channel assignment
Information Sciences: an International Journal
A new collaborative evolutionary-swarm optimization technique
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Conjugate schema and basis representation of crossover and mutation operators
Evolutionary Computation
Dual-population genetic algorithm for nonstationary optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A Compass to Guide Genetic Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Dynamic diversity control in genetic algorithm for mining unsearched solution space in TSP problems
Expert Systems with Applications: An International Journal
Search space division in GAs using phenotypic properties
Information Sciences: an International Journal
Evolutionary swarm cooperative optimization in dynamic environments
Natural Computing: an international journal
Cunning ant system for quadratic assignment problem with local search and parallelization
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Bi-objective multipopulation genetic algorithm for multimodal function optimization
IEEE Transactions on Evolutionary Computation
Defining and optimizing indicator-based diversity measures in multiobjective search
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Analysis of exploration and exploitation in evolutionary algorithms by ancestry trees
International Journal of Innovative Computing and Applications
Generation of a large variety of 3-dimensional maze problems with a genetic algorithm
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Evolutionary computation and its applications in neural and fuzzy systems
Applied Computational Intelligence and Soft Computing
Performance evaluation of evolutionary heuristics in dynamic environments
Applied Intelligence
Expert Systems with Applications: An International Journal
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
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In this article, we propose a new type of genetic algorithm (GA), the forking GA (fGA), which divides the whole search space into subspaces, depending on the convergence status of the population and the solutions obtained so far. The fGA is intended to deal with multimodal problems that are difficult to solve using conventional GAs. We use a multi-population scheme that includes one parent population that explores one subspace and one or more child populations exploiting the other subspace. We consider two types of fGAs, depending on the method used to divide the search space. One is the genoqtypic fGA (g-fGA), which defines the search subspace for each subpopulation, depending on the salient schema within the genotypic search space. The other is the phenotypic fGA (p-fGA), which defines a search subspace by a neighborhood hypercube around the current best individual in the phenotypic feature space. Empirical results on complex function optimization problems show that both the g-fGA and the p-GA perform well compared to conventional GAs. Two additional utilities of the p-fGA are also studied briefly.