NDLI: Generalized crowding for genetic algorithms

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Particle swarm optimization with triggered mutation and its implementation based on GPU

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Cooperative self-organization in a heterogeneous swarm robotic system

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A first assessment of the use of extended relational alphabets in accuracy classifier systems

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Neuronal replicators solve the stability-plasticity dilemma

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Solving the DNA fragment assembly problem efficiently using iterative optimization with evolved hypermutations

Consultant-guided search: a new metaheuristic for combinatorial optimization problems

Beating exhaustive search at its own game: revisiting evolutionary mastermind

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The maximum hypervolume set yields near-optimal approximation

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Object-level recombination of commodity applications

Biogeography-based optimization of neuro-fuzzy system parameters for diagnosis of cardiac disease

Generalized crowding for genetic algorithms

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Generalized crowding for genetic algorithms

Content Provider	ACM Digital Library
Author	Galan, Severino F. Mengshoel, Ole J.
Abstract	Crowding is a technique used in genetic algorithms to preserve diversity in the population and to prevent premature convergence to local optima. It consists of pairing each offspring with a similar individual in the current population (pairing phase) and deciding which of the two will remain in the population (replacement phase). The present work focuses on the replacement phase of crowding, which usually has been carried out by one of the following three approaches: Deterministic, Probabilistic, and Simulated Annealing. These approaches present some limitations regarding the way replacement is conducted. On the one hand, the first two apply the same selective pressure regardless of the problem being solved or the stage of the genetic algorithm. On the other hand, the third does not apply a uniform selective pressure over all the individuals in the population, which makes the control of selective pressure over the generations somewhat difficult. This work presents a Generalized Crowding approach that allows selective pressure to be controlled in a simple way in the replacement phase of crowding, thus overcoming limitations of the other approaches. Furthermore, the understanding of existing approaches is greatly improved, since both Deterministic and Probabilistic Crowding turn out to be special cases of Generalized Crowding. In addition, the temperature parameter used in Simulated Annealing is replaced by a parameter called scaling factor that controls the selective pressure applied. Theoretical analysis using Markov chains and empirical evaluation using Bayesian networks demonstrate the potential of this novel Generalized Crowding approach.
Starting Page	775
Ending Page	782
Page Count	8
File Format	PDF
ISBN	9781450300728
DOI	10.1145/1830483.1830620
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-07-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Markov chain analysis Deterministic crowding Niching Bayesian networks Experiments Probabilistic crowding Genetic algorithms
Content Type	Text
Resource Type	Article

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