Fuzzy Control of Population Size in Evolutionary Algorithms

2006 ◽  
pp. 43-48
Keyword(s):  
Evolutionary Algorithms ◽  
Fuzzy Control ◽  
Population Size

scholarly journals Mediative Fuzzy Logic for Controlling Population Size in Evolutionary Algorithms

2009 ◽  
Vol 01 (02) ◽  
pp. 108-119
Author(s):  
Oscar MONTIEL ◽  
Oscar CASTILLO ◽  
Patricia MELIN ◽  
Roberto SEPULVEDA
Keyword(s):  
Fuzzy Logic ◽  
Evolutionary Algorithms ◽  
Population Size

scholarly journals Hybridization of multi-objective evolutionary algorithms and fuzzy control for automated construction, tuning, and analysis of neuronal models

2013 ◽  
Vol 14 (Suppl 1) ◽  
pp. P369 ◽  
Author(s):  
Parth Patel ◽  
Myles Johnson-Gray ◽  
Emlyne Forren ◽  
Atish Malik ◽  
Tomasz G Smolinski
Keyword(s):  
Evolutionary Algorithms ◽  
Fuzzy Control ◽  
Multi Objective

scholarly journals A Revisit of Infinite Population Models for Evolutionary Algorithms on Continuous Optimization Problems

2020 ◽  
Vol 28 (1) ◽  
pp. 55-85
Author(s):  
Bo Song ◽  
Victor O.K. Li
Keyword(s):  
Population Dynamics ◽  
Evolutionary Algorithms ◽  
Population Size ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Analytical Framework ◽  
Population Models ◽  
Initial Population ◽  
Infinite Population ◽  
Continuous Optimization Problems

Infinite population models are important tools for studying population dynamics of evolutionary algorithms. They describe how the distributions of populations change between consecutive generations. In general, infinite population models are derived from Markov chains by exploiting symmetries between individuals in the population and analyzing the limit as the population size goes to infinity. In this article, we study the theoretical foundations of infinite population models of evolutionary algorithms on continuous optimization problems. First, we show that the convergence proofs in a widely cited study were in fact problematic and incomplete. We further show that the modeling assumption of exchangeability of individuals cannot yield the transition equation. Then, in order to analyze infinite population models, we build an analytical framework based on convergence in distribution of random elements which take values in the metric space of infinite sequences. The framework is concise and mathematically rigorous. It also provides an infrastructure for studying the convergence of the stacking of operators and of iterating the algorithm which previous studies failed to address. Finally, we use the framework to prove the convergence of infinite population models for the mutation operator and the [Formula: see text]-ary recombination operator. We show that these operators can provide accurate predictions for real population dynamics as the population size goes to infinity, provided that the initial population is identically and independently distributed.

scholarly journals Population Size Influence on the Efficiency of Evolutionary Algorithms to Design Water Networks

2017 ◽  
Vol 186 ◽  
pp. 341-348 ◽  
Author(s):  
Daniel Mora-Melià ◽  
F. Javier Martínez-Solano ◽  
Pedro L. Iglesias-Rey ◽  
Jimmy H. Gutiérrez-Bahamondes
Keyword(s):  
Evolutionary Algorithms ◽  
Population Size ◽  
Water Networks

scholarly journals On the Choice of the Offspring Population Size in Evolutionary Algorithms

2005 ◽  
Vol 13 (4) ◽  
pp. 413-440 ◽  
Author(s):  
Thomas Jansen ◽  
Kenneth A. De Jong ◽  
Ingo Wegener
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Population Size ◽  
Diffi Cult ◽  
Very High

Evolutionary algorithms (EAs) generally come with a large number of parameters that have to be set before the algorithm can be used. Finding appropriate settings is a diffi- cult task. The influence of these parameters on the efficiency of the search performed by an evolutionary algorithm can be very high. But there is still a lack of theoretically justified guidelines to help the practitioner find good values for these parameters. One such parameter is the offspring population size. Using a simplified but still realistic evolutionary algorithm, a thorough analysis of the effects of the offspring population size is presented. The result is a much better understanding of the role of offspring population size in an EA and suggests a simple way to dynamically adapt this parameter when necessary.

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