Results 1 
4 of
4
PopulationBased Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
, 1994
"... Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within th ..."
Abstract

Cited by 298 (11 self)
 Add to MetaCart
Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within the framework of competitive learning. This new perspective reveals a number of different possibilities for performance improvements. This paper explores populationbased incremental learning (PBIL), a method of combining the mechanisms of a generational genetic algorithm with simple competitive learning. The combination of these two methods reveals a tool which is far simpler than a GA, and which outperforms a GA on large set of optimization problems in terms of both speed and accuracy. This paper presents an empirical analysis of where the proposed technique will outperform genetic algorithms, and describes a class of problems in which a genetic algorithm may be able to perform better. Extensions to this algorithm are discussed and analyzed. PBIL and extensions are compared with a standard GA on twelve problems, including standard numerical optimization functions, traditional GA test suite problems, and NPComplete problems.
Population Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitve Learning
, 1994
"... Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within ..."
Abstract

Cited by 30 (0 self)
 Add to MetaCart
Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within the framework of competitive learning. This new perspective reveals a number of different possibilities for performance improvements. This paper explores population based incremental learning (PBIL), a method of combining the mechanisms of a generational genetic algorithm with simple competitive learning. The combination of these two methods reveals a tool which is far simpler than a GA, and which outperforms a GA on large set of optimization problems in terms of both speed and accuracy. This paper presents an empirical analysis of where the proposed technique will outperform genetic algorithms, and describes a class of problems in which a genetic algorithm may be able to perform b...
Connectionist theory refinement: Genetically searching the space of network topologies
 Journal of Artificial Intelligence Research
, 1997
"... An algorithm that learns from a set of examples should ideally be able to exploit the available resources of (a) abundant computing power and (b) domainspecific knowledge to improve its ability to generalize. Connectionist theoryrefinement systems, which use background knowledge to select a neural ..."
Abstract

Cited by 29 (1 self)
 Add to MetaCart
An algorithm that learns from a set of examples should ideally be able to exploit the available resources of (a) abundant computing power and (b) domainspecific knowledge to improve its ability to generalize. Connectionist theoryrefinement systems, which use background knowledge to select a neural network's topology and initial weights, have proven to be effective at exploiting domainspecific knowledge; however, most do not exploit available computing power. This weakness occurs because they lack the ability to refine the topology of the neural networks they produce, thereby limiting generalization, especially when given impoverished domain theories. We present the REGENT algorithm which uses (a) domainspecific knowledge to help create an initial population of knowledgebased neural networks and (b) genetic operators of crossover and mutation (specifically designed for knowledgebased networks) to continually search for better network topologies. Experiments on three realworld domains indicate that our new algorithm is able to significantly increase generalization compared to a standard connectionist theoryrefinement system, as well as our previous algorithm for growing knowledgebased networks.