Micro-Genetic Algorithms For Stationary And Non-Stationary Function Optimization

1990 ◽  
Author(s):  
Kalmanje Krishnakumar
Keyword(s):  
Genetic Algorithms ◽  
Function Optimization ◽  
Stationary Function

scholarly journals A Novel Mating Approach for Genetic Algorithms

2013 ◽  
Vol 21 (2) ◽  
pp. 197-229 ◽  
Author(s):  
Severino F. Galán ◽  
Ole J. Mengshoel ◽  
Rafael Pinter
Keyword(s):  
Genetic Algorithms ◽  
Random Mating ◽  
Adaptive Strategy ◽  
Mating Strategies ◽  
Function Optimization ◽  
The Novel ◽  
Independent Manner ◽  
Novel Approach ◽  
Domain Independent ◽  
Self Adaptive

Genetic algorithms typically use crossover, which relies on mating a set of selected parents. As part of crossover, random mating is often carried out. A novel approach to parent mating is presented in this work. Our novel approach can be applied in combination with a traditional similarity-based criterion to measure distance between individuals or with a fitness-based criterion. We introduce a parameter called the mating index that allows different mating strategies to be developed within a uniform framework: an exploitative strategy called best-first, an explorative strategy called best-last, and an adaptive strategy called self-adaptive. Self-adaptive mating is defined in the context of the novel algorithm, and aims to achieve a balance between exploitation and exploration in a domain-independent manner. The present work formally defines the novel mating approach, analyzes its behavior, and conducts an extensive experimental study to quantitatively determine its benefits. In the domain of real function optimization, the experiments show that, as the degree of multimodality of the function at hand grows, increasing the mating index improves performance. In the case of the self-adaptive mating strategy, the experiments give strong results for several case studies.

A Species Conserving Genetic Algorithm for Multimodal Function Optimization

2002 ◽  
Vol 10 (3) ◽  
pp. 207-234 ◽  
Author(s):  
Jian-Ping Li ◽  
Marton E. Balazs ◽  
Geoffrey T. Parks ◽  
P. John Clarkson
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Multiple Solutions ◽  
Species Conservation ◽  
Function Optimization ◽  
Test Problems ◽  
Next Generation ◽  
Multimodal Function Optimization ◽  
Multimodal Function ◽  
A New Technique

This paper introduces a new technique called species conservation for evolving paral-lel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current gen-eration are saved (conserved) by moving them into the next generation. Our technique has proved to be very effective in finding multiple solutions of multimodal optimiza-tion problems. We demonstrate this by applying it to a set of test problems, including some problems known to be deceptive to genetic algorithms.

Rational function optimization using genetic algorithms

2007 ◽  
Vol 9 (4) ◽  
pp. 403-413 ◽  
Author(s):  
M.J. Valadan Zoej ◽  
M. Mokhtarzade ◽  
A. Mansourian ◽  
H. Ebadi ◽  
S. Sadeghian
Keyword(s):  
Genetic Algorithms ◽  
Rational Function ◽  
Function Optimization

Researches on Function Optimization Problems Based on Chaotic Immune Genetic Algorithms

2012 ◽  
Vol 616-618 ◽  
pp. 2064-2067
Author(s):  
Yong Gang Che ◽  
Chun Yu Xiao ◽  
Chao Hai Kang ◽  
Ying Ying Li ◽  
Li Ying Gong
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Chaotic Map ◽  
Solution Space ◽  
Population Diversity ◽  
Function Optimization ◽  
Initial Population ◽  
Immune Genetic Algorithm ◽  
Complex Functions

To solve the primary problems in genetic algorithms, such as slow convergence speed, poor local searching capability and easy prematurity, the immune mechanism is introduced into the genetic algorithm, and thus population diversity is maintained better, and the phenomena of premature convergence and oscillation are reduced. In order to compensate the defects of immune genetic algorithm, the Hénon chaotic map, which is introduced on the above basis, makes the generated initial population uniformly distributed in the solution space, eventually, the defect of data redundancy is reduced and the quality of evolution is improved. The proposed chaotic immune genetic algorithm is used to optimize the complex functions, and there is an analysis compared with the genetic algorithm and the immune genetic algorithm, the feasibility and effectiveness of the proposed algorithm are proved from the perspective of simulation experiments.

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