Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator
Proceedings of the 3rd International Conference on Genetic Algorithms
Fuzzy Recombination for the Breeder Genetic Algorithm
Proceedings of the 6th International Conference on Genetic Algorithms
An analysis on crossovers for real number chromosomes in an infinite population size
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
A new generation alternation model for differential evolution
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Real-Valued LCS Using UNDX for Technology Extraction
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Evolutionary Multi-Objective Optimization for Biped Walking
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
An analysis of heterogeneous cooperative algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Rotationally invariant crossover operators in evolutionary multi-objective optimization
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
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This chapter presents a real-coded genetic algorithm using the Unimodal Normal Distribution Crossover (UNDX) that can efficiently optimize functions with epistasis among parameters. Most conventional crossover operators for function optimization have been reported to have a serious problem in that their performance deteriorates considerably when they are applied to functions with epistasis among parameters. We believe that the reason for the poor performance of the conventional crossover operators is that they cannot keep the distribution of individuals unchanged in the process of repetitive crossover operations on functions with epistasis among parameters. In considering the above problem, we introduce three guidelines, 'Preservation of Statistics', 'Diversity of Offspring', and 'Enhancement of Robustness', for designing crossover operators that show good performance even on epistatic functions. We show that the UNDX meets the guidelines very well by a theoretical analysis and that the UNDX shows better performance than some conventional crossover operators by applying them to some benchmark functions including multimodal and epistatic ones. We also discuss some improvements of the UNDX under the guidelines and the relation between real-coded genetic algorithms using the UNDX and evolution strategies (ESs) using the correlated mutation.