Adaptive isolation model using data clustering for multimodal function optimization

Authors:
Shin Ando;Jun Sakuma;Shigenobu Kobayashi
Affiliations:
Yokohama National University, Kanagawa, Japan;Tokyo Institute of Technology, Kanagawa, Japan;Tokyo Institute of Technology, Kanagawa, Japan
Venue:
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Year:
2005

Citing 3
Cited 5

A species conserving genetic algorithm for multimodal function optimization

Evolutionary Computation
A computationally efficient evolutionary algorithm for real-parameter optimization

Evolutionary Computation
An analysis of the behavior of a class of genetic adaptive systems.

An analysis of the behavior of a class of genetic adaptive systems.

Heuristic speciation for evolving neural network ensemble

Proceedings of the 9th annual conference on Genetic and evolutionary computation
The crowding approach to niching in genetic algorithms

Evolutionary Computation
A new real-coded genetic algorithm using the adaptive selection network for detecting multiple optima

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Adaptive niche radii and niche shapes approaches for niching with the cma-es

Evolutionary Computation
Taking advantage of a diverse set of efficient production schedules: A two-step approach for scheduling with side concerns

Computers and Operations Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a GA model called Adaptive Isolation Model(AIM), for multimodal optimization. It uses a data clustering algorithm to detect clusters in GA population, which identifies the attractors in the fitness landscape. Then, subpopulations which makes-up the clusters are isolated and optimized independently. Meanwhile, the region of the isolated subpopulations in the original landscape are suppressed. The isolation increases comprehensiveness, i.e., the probability of finding weaker attractors, and the overall efficiency of multimodal search. The advantage of the AIM is that it does not require distance between the optima as a presumed parameter, as it is estimated from the variance/covariance matrix of the subpopulation.Further, AIM's behavior and efficiency is equivalent to basic GA in unimodal landscape, in terms of number of evaluation. Therefore, it is applied recursively to all subpopulations until they converge to a suboptima. This makes AIM suitable for locally-multimodal landscapes, which have closely located attractors that are difficult to distinguish in the initial run.The performance of AIM is evaluated in several benchmark problems and compared to iterated hill-climbing methods.