A building block favoring reordering method for gene positions in genetic algorithms
Explore this paper's citation graph
Summary
This work proposes an algorithm to speedup convergence of genetic algorithms that is based on the investigation of neighbouring gene values of the successful individuals of the chromosome pool, based on some statistical inference on neighbour gene values.
- Type
- article
- Published
- 2001-07-07
- Cited by
- 13
- References
- 7
- OpenAlex
- https://openalex.org/W22943649
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:767470
Keywords
Block (permutation group theory), Algorithm, Speedup, Chromosome, Computer science
References
- Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence
- Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms
- Evolutionary algorithms in theory and practice
- An introduction to genetic algorithms
- An overview of Genetic Algorithms: Pt1, Fundamentals
- Genetic Algorithms in Search Optimization and Machine Learning
- An Overview of Genetic Algorithms: Part 2, Research Topics
Cited by
- A Genetic Algorithm With Self-distancing Bits But No Overt Linkage
- Voronoi quantized crossover for traveling salesman problem
- Partnering Strategies For Fitness Evaluation In A Pyramidal Evolutionary Algorithm
- The transformation of the k-Shortest Steiner trees search problem into binary dynamic problem for effective evolutionary methods application
- Subpopulation initialization driven by linkage learning for dealing with the Long-Way-To-Stuck effect
- Empirical Linkage Learning
- Empirical problem decomposition - the key to the evolutionary effectiveness in solving a large-scale non-binary discrete real-world problem
- From Direct to Directional Variable Dependencies—Nonsymmetrical Dependencies Discovery in Real-World and Theoretical Problems
- Gene Reordering and Concurrency in Genetic Algorithms
- A Survey of Linkage Learning Techniques in Genetic and Evolutionary Algorithms
- Evaluating the Seeding Genetic Algorithm
- Gene Level Concurrency in Genetic Algorithms
- Evolving Genotype to Phenotype Mappings with a Multiple-Chromosome Genetic Algorithm
Related papers
- Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence
- A block-based evolutionary algorithm for flow-shop scheduling problem
- Searching for the optimal coding in genetic algorithms
- A Hierarchical Gene-Set Genetic Algorithm
- Gene-orientation operator for genetic algorithm
- Representational redundancy in evolutionary algorithms
- Application of Genetic Algorithm to Optimization of One-Dimensional Cutting-Stock without Replicated Sizes
- Genetic algorithm with local search for the unrelated parallel machine scheduling problem with sequence-dependent set-up times
- Sequential Structuring Element for CFG Induction Using Genetic Algorithm