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Conference

international conference on Genetic algorithms 

About: international conference on Genetic algorithms is an academic conference. The conference publishes majorly in the area(s): Genetic algorithm & Genetic representation. Over the lifetime, 524 publications have been published by the conference receiving 61114 citations.

Papers published on a yearly basis

Papers
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Proceedings Article
01 Jun 1993
TL;DR: A rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs) and the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM.
Abstract: The paper describes a rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to allow direct intervention of an external decision maker (DM). Finally, the MOGA is generalised further: the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM. It is the interaction between the two that leads to the determination of a satisfactory solution to the problem. Illustrative results of how the DM can interact with the genetic algorithm are presented. They also show the ability of the MOGA to uniformly sample regions of the trade-off surface.

2,788 citations

Proceedings Article
01 Jun 1989

2,164 citations

Proceedings Article
01 Oct 1987
TL;DR: In this article, the authors developed and investigated the method of sharing functions to permit the formation of stable subpopulations of different strings within a GA, thereby permitting the parallel investigation of many peaks.
Abstract: Many practical search and optimization problems require the investigation of multiple local optima. In this paper, the method of sharing functions is developed and investigated to permit the formation of stable subpopulations of different strings within a genetic algorithm (CA), thereby permitting the parallel investigation of many peaks. The theory and implementation of the method are investigated and two, one-dimensional test functions are considered. On a test function with five peaks of equal height, a GA without sharing loses strings at all but one peak; a GA with sharing maintains roughly equally sized subpopulations clustered about all five peaks. On a test function with five peaks of different sizes, a GA without sharing loses strings at all but the highest peak; a GA with sharing allocates decreasing numbers of strings to peaks of decreasing value as predicted by theory.

2,154 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
199799
19961
1995167
19941
1993116
199114