A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An investigation of niche and species formation in genetic function optimization
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Niching methods for genetic algorithms
Niching methods for genetic algorithms
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
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Comparison of multi-modal optimization algorithms based on evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Adaptive genetic algorithm with mutation and crossover matrices
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Mutation matrix in evolutionary computation: an application to resource allocation problem
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
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A histogram assisted adjustment of fitness distribution in standard genetic algorithm is introduced and tested on four benchmark functions of complex landscapes, with remarkable improvement in performance, such as the substantial enhancement in the probability of detecting local minima. Numerical tests suggest that the idea of histogram assisted adjustment, or the "renormalization" of the fitness distribution, is generally advantageous for multi-modal function optimization. An analysis on the effect of the bin number of the histogram has also been carried out, showing that the performance of the algorithm is insensitive to this extra parameter as long as it is an order of magnitude smaller than the size of the population (N) in the genetic algorithm. This analysis suggests that the advantage of the introduction of histogram assisted fitness adjustment is a robust feature for genetic algorithm, since the adjustment of fitness enhances exploration by broadening the diversity of the population of chromosomes. In general, the advantage of this histogram assisted adjustment more than compensates the cost of computation resource in the construction of the histogram with O(N) time complexity. Suggestions of using this technique for the mapping of complex landscape are discussed.