Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
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
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Steady-state evolutionary algorithm for multimodal function global optimization
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
| Hi-index | 0.00 |
This paper presents a population climbing evolutionary algorithm (PCEA) for solving function optimization containing multiple global optima. The algorithm combines a multi-parent crossover operator with the complete local search. The multi-parent crossover operator can enables individual to draw closer to each optimal solution,thus the population will be divided into subpopulations automatically , meanwhile, the local search is adopted to enable individual to converge to the nearest optimal solution which belongs to the same attractor. By this way, each individuals can converge to a global optima, then the population can maintain all global optima. Comparing with other algorithms, it has the following advantages.(1) The algorithm is very simple with little computation complexity .(2) Proposed algorithm needs no additional control parameter which depends on a special problem. The experiment results show that PCEA is very efficient for the optimization of multimodal functions, usually it can obtain all the global optimal solutions by running once of the algorithm.