Decision making in a hybrid genetic algorithm.
Lobo, F. G., & Goldberg, D. E. (1996)TR No.: 96009 | Download PDF | Download PS
Abstract:
There are several issues that need to be taken in consideration when designing a hybrid problem solver. This paper focus on one of them — decision making. More specifically, we address the following questions: given two different methods, how to get the most out of both of them? When should we use one and when […]
Posted: February 4th, 1996 under Genetic algorithms. Comments: none
Predicting speedups of idealized bounding cases of parallel genetic algorithms.
Cantú-Paz, E., & Goldberg, D. E. (1996)TR No.: 96008 | Download PDF | Download PS
Abstract:
This paper presents models that predict the speedup of two cases that bound the possible topologies and migration rates of parallel genetic algorithms (GAs). The first bounding case is a parallel GA with completely isolated demes or subpopulations. The model for this case shows that the speedup is not very significant as more demes are […]
Posted: January 24th, 1996 under Genetic algorithms. Comments: none
Modeling idealized bounding cases of parallel genetic algorithms.
Cantú-Paz, E., & Goldberg, D. E (1996)TR No.: 96007 | Download PDF | Download PS
Abstract:
This paper presents models to predict the quality of convergence of idealized bounding cases of parallel genetic algorithms (GAs). The first bounding case is a parallel GA with completely isolated subpopulations (demes). We show how the probability that the parallel GA finds a solution of the minimum desired quality increases as more demes are used. […]
Posted: January 20th, 1996 under Genetic algorithms. Comments: none
Learning linkage. 14 pp.
Harik, G., & Goldberg, D. E. (1996)TR No.: 96006 | Download PDF | Download PS
Abstract:
Related PostsA Survey of Linkage Learning Techniques in Genetic and Evolutionary AlgorithmsTightness Time for the Linkage Learning Genetic AlgorithmLinkage learning via probabilistic modeling in the ECGA
Posted: January 16th, 1996 under Genetic algorithms. Comments: none
Optimal sampling for genetic algorithms. 8 pp.
Miller, B. L., & Goldberg, D. E. (1996)TR No.: 96005 | Download PDF | Download PS
Abstract:
Abstract:
In many real-world environments, a genetic algorithm designer is often faced with choosing the best fitness function from a range of possibilities. Fitness functions differ primarily based upon the speed, accuracy, and cost of a fitness evaluation. An important type of fitness function is the \textit{sampling fitness function}, which utilizes sampling in order to reduce […]
Posted: January 12th, 1996 under Genetic algorithms. Comments: none
The gambler’s ruin problem. Genetic algorithms and the sizing of populations. 17pp.
Harik, G., Cantú-Paz, E., Goldberg, D. E., & Miller, B. L. (1996)TR No.: 96004 | Download PDF | Download PS
Abstract:
This paper presents a model for predicting the convergence quality of genetic algorithms. The model incorporates previous knowledge about decision making in genetic algorithms and the initial supply of building blocks in a novel way. The result is an equation that accurately predicts the quality of the solution found by a GA using a given […]
Posted: January 8th, 1996 under Genetic algorithms. Comments: none