Genetic Algorithms at the University of Illinois
Goldberg, D.E. (ed.) (1999)TR No.: 99012 | Download PDF | Download PS
Abstract:
Related PostsGenetic Algorithms at the University of Illinois Fall 1999Genetic Algorithms at the University of Illinois Spring 2000Genetic Algorithms at the University of Illinois Fall 2000
Posted: March 4th, 1999 under Genetic algorithms. Comments: none
A Simple Implementation of the Bayesian Optimization Algorithm in C++
Pelikan, M. (1999)TR No.: 99011 | Download PDF | Download PS
Abstract:
The paper explains how to download, compile, and use the simple implementation of the Bayesian optimization algorithm (BOA), version 1.0, written in C++. It provides the instructions for creating input files for the BOA to solve various problems with various parameter settings and for adding new test functions into the existing code. Outputs of an […]
Posted: February 24th, 1999 under Genetic algorithms. Comments: none
Linkage learning via probabilistic modeling in the ECGA
Harik, G. (1999)TR No.: 99010 | Download PDF | Download PS
Abstract:
The goal of linkage learning, or building block identification, is the creation of a more effective genetic algorithm (GA). This paper explores the relationship between the linkage-learning problem and that of learning probability distributions over multi-variate spaces. Herein, it is argued that these problems are equivalent. Using a simple but effective approach to learning distributions, […]
Posted: February 20th, 1999 under Genetic algorithms. Comments: none
A parameter-less genetic algorithm.
Harik, G. and Lobo, F. (1999)TR No.: 99009 | Download PDF | Download PS
Abstract:
From the user’s point of view, setting the parameters of a genetic algorithm (GA) is far from a trivial task. Moreover, the user is typically not interested in population sizes, crossover probabilities, selection rates, and other GA technicalities. He is just interested in solving a problem, and what he would really like to do, is […]
Posted: February 16th, 1999 under Genetic algorithms. Comments: none
Migration policies and takeover times in parallel genetic algorithms.
Cantú-Paz, E. (1999)TR No.: 99008 | Download PDF | Download PS
Abstract:
This paper investigates how the choices of migrants and replacements affect the takeover time in a parallel genetic algorithm with multiple populations. The analysis is independent of the number of populations or the topology of communications. Migration is assumed to occur every generation, and the number of migrants remains constant for the duration of the […]
Posted: February 12th, 1999 under Genetic algorithms. Comments: none
Topologies, migration rates and multi-population parallel genetic algorithms.
Cantú-Paz, E. (1999)TR No.: 99007 | Download PDF | Download PS
Abstract:
This paper presents an analysis of parallel genetic algorithms (GAs) with multiple populations (demes). The analysis makes explicit the relation between the probability of reaching a desired solution with the deme size, the migration rate, and the degree of the connectivity graph. The analysis considers arbitrary topologies with a fixed number of neighbors per deme. […]
Posted: February 8th, 1999 under Genetic algorithms. Comments: none
Parallel genetic algorithms with distributed panmictic populations.
Cantú-Paz, E. and Goldberg, D.E. (1999)TR No.: 99006 | Download PDF | Download PS
Abstract:
Genetic algorithms (GAs) are commonly parallelized using multiple communicating populations or by keeping one population and dividing the task of evaluating the fitness among several processors. This paper examines an algorithm where the population is physically distributed, but behaves like a single panmictic unit. This is a desirable property because much more is known about […]
Posted: February 4th, 1999 under Genetic algorithms. Comments: none
Linkage identification by non-monotonicity detection for overlapping functions
Munetomo, M., Goldberg, D.E (1999)TR No.: 99005 | Download PDF | Download PS
Abstract:
This paper presents the linkage identification by non-monotonicity detection (LIMD) procedure and its extension for overlapping functions by introducing the tightness detection (TD) procedure. The LIMD identifies linkage groups directly by performing order-2 simultaneous perturbations on a pair of loci to detect monotonicity/non-monotonicity of fitness changes. The LIMD can identify linkage groups with at most […]
Posted: January 24th, 1999 under Genetic algorithms. Comments: none
Probabilistic crowding: deterministic crowding with probabilistic replacement
Menshoel, O.J., Goldberg, D.E. (1999)TR No.: 99004 | Download PDF | Download PS
Abstract:
This paper presents a novel niching algorithm, probabilistic crowding. Like its predecessor deterministic crowding, probabilistic crowding is fast, simple, and requires no parameters beyond that of the classical GA. In probabilistic crowding, subpopulations are maintained reliably, and it is possible to analyze and predict how this maintenance takes place. This paper also identifies probabilistic crowding […]
Posted: January 20th, 1999 under Genetic algorithms. Comments: none
BOA: The Bayesian optimization algorithm.
Pelikan, M., Goldberg, D.E., and Cantú-Paz, E. (1999)TR No.: 99003 | Download PDF | Download PS
Abstract:
In this paper, an algorithm based on the concepts of genetic algorithms that uses an estimation of the joint distribution of promising solutions in order to generate new candidate solutions is proposed. The proposed algorithm is called the Bayesian optimization algorithm (BOA). To estimate the distribution of promising solutions, techniques for modeling multivariate data by […]
Posted: January 16th, 1999 under Genetic algorithms. Comments: none