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We present the first results obtained from two implementations of a hybrid architecture which balances exploration and exploitation to solve mazes with continuous search spaces. In both cases the critic is based around a Radial Basis Function (RBF) Neural Network which uses Temporal Difference learning to acquire a continuous valued internal model of the environment through interaction with it. Also...
It has long been recognised that the choice of recombination and mutation operators and the rates at which they are applied to a Genetic Algorithm will have a significant effect on the outcome of the evolutionary search, with sub-optimal values often leading to poor performance. In this paper an evolutionary algorithm (APES) is presented within which both the units of heredity and the probability...
A genetic recombination framework is presented within which both the unit of inheritance of genetic material from a parent, and the number of parents involved in the creation of a new individual are potentially learnt through the evolution of competing subpopulations representing different strategies. At the heart of the framework is a recombination mechanism whereby a newly created member...
This paper examines the use of genetic algorithms (GAs) in generating sets of input data to use for software testing. The aim is to produce test sets which maximise coverage of the software using a given metric, whilst minimising the size of the sets. Using the well known triangle program as an example, a representation is described which allows the GA to learn the number of test cases in a...
In this paper we examine a modification to the genetic algorithm — a new adaptive operator was developed for two industrial applications using genetic algorithm based on-line control systems. The aim is to enable the control systems to track optima of a time-varying dynamic system whilst not being detrimental to its ability to provide sound results for the stationary environments. When compared with...
In this paper we present a new version of our previous work on a maze learning animat. Its sensory/motor capabilities have been extended and modified so that they are more biologically plausible than before. The animat's learning architecture is based around a hybrid RBF Neural Network/Evolutionary Strategy implementation of an Adaptive Heuristic Critic. We conduct experiments in which the animat...
The objective of this study is a comparison of two models of a genetic algorithm — the generational and incremental/steady state genetic algorithms — for use in the nonstationary/dynamic environments. It is experimentally shown that selection of a suitable version of the genetic algorithm can improve performance of the genetic algorithm in such environments.This can extend ability of the genetic algorithm...
AbstractGenetic Algorithms are a class of powerful, robust search techniques based on genetic inheritance and the Darwinian metaphor of Natural Selection. These algorithms maintain a finite memory of individual points on the search landscape known as the population. Members of the population are usually represented as strings written over some fixed alphabet, each of which has a scalar value attached...
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