scholarly journals Behavior of finite population variable length genetic algorithms under random selection

2005 ◽  
Author(s):  
Hal Stringer ◽  
Annie S. Wu
Keyword(s):  
Genetic Algorithms ◽  
Finite Population ◽  
Variable Length ◽  
Random Selection ◽  
Population Variable

scholarly journals The effect of random selection coefficients on populations of finite size – some particular models

1977 ◽  
Vol 29 (2) ◽  
pp. 97-112 ◽  
Author(s):  
P. J. Avery
Keyword(s):  
Finite Population ◽  
Finite Size ◽  
Random Selection ◽  
Heterozygote Advantage ◽  
Expected Heterozygosity ◽  
Reasonable Degree ◽  
Equal Variance ◽  
The Mean ◽  
Mutant Genes ◽  
Steady State Solutions

SUMMARYThe model of random selection coefficients is considered in the context of a finite population of diploids. The selection coefficients of the homozygotes are allowed to vary with equal variance while the fitness of the heterozygote is kept fixed. Steady-state solutions are found in the case of equal two-way mutation rates with particular reference to the expected heterozygosity. Increasing the variance of the selection coefficients of the homozygotes is found to uniformly increase the heterozygosity for all values of the average selection coefficients and its effect is largest when the selection coefficients of the homozygotes are fully correlated. The fate of mutant genes is also considered in the case of random selection coefficients by looking at the probability of ultimate fixation and the mean times to fixation and extinction. The errors in previous calculations (e.g. Kimura, 1954; Ohta, 1972) are pointed out. It is found that a small average heterozygote advantage together with a reasonable degree of variance in the coefficients can cause an unexpectedly large amount of heterozygosity to be maintained. It is also seen that probabilities of fixation and mean times to boundaries are usually increased by increasing the variance showing that it in fact helps to keep the population heterozygous for much longer than the non-random case. This is in contradiction to some conclusions of Karlin & Levikson (1974) because their haploid results are not easily extendable to the consideration of this sort of diploid model.

scholarly journals Solving the container pre-marshalling problem using variable length genetic algorithms

2015 ◽  
Vol 48 (4) ◽  
pp. 687-705 ◽  
Author(s):  
Mohamed Gheith ◽  
Amr B. Eltawil ◽  
Nermine A. Harraz
Keyword(s):  
Genetic Algorithms ◽  
Variable Length

Analysis of Selection Algorithms: A Markov Chain Approach

1996 ◽  
Vol 4 (2) ◽  
pp. 133-167 ◽  
Author(s):  
Uday Kumar Chakraborty ◽  
Kalyanmoy Deb ◽  
Mandira Chakraborty
Keyword(s):  
Genetic Algorithms ◽  
Markov Chain ◽  
Performance Measures ◽  
Finite Population ◽  
Selection Strategy ◽  
Markov Chain Approach ◽  
Population Sizes ◽  
Tournament Selection ◽  
Selection Algorithms ◽  
Compare And Contrast

A Markov chain framework is developed for analyzing a wide variety of selection techniques used in genetic algorithms (GAs) and evolution strategies (ESs). Specifically, we consider linear ranking selection, probabilistic binary tournament selection, deterministic s-ary (s = 3,4, …) tournament selection, fitness-proportionate selection, selection in Whitley's GENITOR, selection in (μ, λ)-ES, selection in (μ + λ)-ES, (μ, λ)-linear ranking selection in GAs, (μ + λ)-linear ranking selection in GAs, and selection in Eshelman's CHC algorithm. The analysis enables us to compare and contrast the various selection algorithms with respect to several performance measures based on the probability of takeover. Our analysis is exact—we do not make any assumptions or approximations. Finite population sizes are considered. Our approach is perfectly general, and following the methods of this paper, it is possible to analyze any selection strategy in evolutionary algorithms.

Modeling Simple Genetic Algorithms

1995 ◽  
Vol 3 (4) ◽  
pp. 453-472 ◽  
Author(s):  
Michael D. Vose
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Asymptotic Behavior ◽  
Finite Population ◽  
Large Population ◽  
Population Models ◽  
Genetic Search ◽  
Simple Genetic Algorithm ◽  
Simple Genetic Algorithms

The infinite- and finite-population models of the simple genetic algorithm are extended and unified, The result incorporates both transient and asymptotic GA behavior. This leads to an interpretation of genetic search that partially explains population trajectories. In particular, the asymptotic behavior of the large-population simple genetic algorithm is analyzed.

Solving metameric variable-length optimization problems using genetic algorithms

2016 ◽  
Vol 18 (2) ◽  
pp. 247-277 ◽  
Author(s):  
Matthew L. Ryerkerk ◽  
Ronald C. Averill ◽  
Kalyanmoy Deb ◽  
Erik D. Goodman
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Variable Length ◽  
Length Optimization

scholarly journals A Variable-Length Chromosome Genetic Algorithm for Time-Based Sensor Network Schedule Optimization

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3990
Author(s):  
Van-Phuong Ha ◽  
Trung-Kien Dao ◽  
Ngoc-Yen Pham ◽  
Minh-Hoang Le
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Sensor Network ◽  
Optimal Solution ◽  
Stochastic Approach ◽  
Variable Length ◽  
Sensor Nodes ◽  
Fixed Length ◽  
Network Schedule ◽  
Application Requirements

Scheduling sensor nodes has an important role in real monitoring applications using sensor networks, lowering the power consumption and maximizing the network lifetime, while maintaining the satisfaction to application requirements. Nevertheless, this problem is usually very complex and not easily resolved by analytical methods. In a different manner, genetic algorithms (GAs) are heuristic search strategies that help to find the exact or approximate global optimal solution efficiently with a stochastic approach. Genetic algorithms are advantageous for their robustness to discrete and noisy objective functions, as they are only evaluated at independent points without requirements of continuity or differentiability. However, as explained in this paper, a time-based sensor network schedule cannot be represented by a chromosome with fixed length that is required in traditional genetic algorithms. Therefore, an extended genetic algorithm is introduced with variable-length chromosome (VLC) along with mutation and crossover operations in order to address this problem. Simulation results show that, with help of carefully defined fitness functions, the proposed scheme is able to evolve the individuals in the population effectively and consistently from generation to generation towards optimal ones, and the obtained network schedules are better optimized in comparison with the result of algorithms employing a fixed-length chromosome.

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