Results 1  10
of
471
The GENITOR Algorithm and Selection Pressure: Why RankBased Allocation of Reproductive Trials is Best
 Proceedings of the Third International Conference on Genetic Algorithms
, 1989
"... This paper reports work done over the past three years using rankbased allocation of reproductive trials. New evidence and arguments are presented which suggest that allocating reproductive trials according to rank is superior to fitness proportionate reproduction. Ranking can not only be used to s ..."
Abstract

Cited by 423 (14 self)
 Add to MetaCart
of 1) Holland's schema theorem, 2) DeJong's standard test suite, and 3) a set of neural net optimization problems that are larger than the problems in the standard test suite. The GENITOR algorithm is also discussed; this algorithm is specifically designed to allocate reproductive trials
A Genetic Algorithm Tutorial
 Statistics and Computing
, 1994
"... This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search byhyperplane sampling. The theoretical foundations of genetic algorit ..."
Abstract

Cited by 326 (5 self)
 Add to MetaCart
algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.
General Schema Theory for Genetic Programming with SubtreeSwapping Crossover
 In Genetic Programming, Proceedings of EuroGP 2001, LNCS
, 2001
"... In this paper a new, general and exact schema theory for genetic programming is presented. The theory includes a microscopic schema theorem applicable to crossover operators which replace a subtree in one parent with a subtree from the other parent to produce the offspring. A more macroscopic schema ..."
Abstract

Cited by 54 (30 self)
 Add to MetaCart
In this paper a new, general and exact schema theory for genetic programming is presented. The theory includes a microscopic schema theorem applicable to crossover operators which replace a subtree in one parent with a subtree from the other parent to produce the offspring. A more macroscopic
A ParameterLess Genetic Algorithm
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 1999
"... 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 probl ..."
Abstract

Cited by 284 (35 self)
 Add to MetaCart
aspects of the theory of GAs, including previous research work on population sizing, the schema theorem, building block mixing, and genetic drift.
The Schema Theorem and Price's Theorem
 FOUNDATIONS OF GENETIC ALGORITHMS
, 1995
"... Holland's Schema Theorem is widely taken to be the foundation for explanations of the power of genetic algorithms (GAs). Yet some dissent has been expressed as to its implications. Here, dissenting arguments are reviewed and elaborated upon, explaining why the Schema Theorem has no implicati ..."
Abstract

Cited by 101 (3 self)
 Add to MetaCart
Holland's Schema Theorem is widely taken to be the foundation for explanations of the power of genetic algorithms (GAs). Yet some dissent has been expressed as to its implications. Here, dissenting arguments are reviewed and elaborated upon, explaining why the Schema Theorem has
Convergence Analysis of Canonical Genetic Algorithms
 IEEE Transactions on Neural Networks
, 1994
"... This paper analyzes the convergence properties of the canonical genetic algorithm (CGA) with mutation, crossover and proportional reproduction applied to static optimization problems. It is proved by means of homogeneous finite Markov chain analysis that a CGA will never converge to the global optim ..."
Abstract

Cited by 238 (0 self)
 Add to MetaCart
nonconvergent CGA. These results are discussed with respect to the schema theorem. Keywords: canonical genetic algorithm, global convergence, Markov chains, schema theorem 1 Introduction Canonical genetic algorithms (CGA) as introduced in [1] are often used to tackle static optimization problems of the type
A Macroscopic Exact Schema Theorem and a Redefinition of Effective Fitness for GP with OnePoint Crossover
, 2000
"... This paper extends recent results in the GP schema theory by formulating a proper exact schema theorem for GP with onepoint crossover. This gives an exact expression for the expected number of instances of a schema at the next generation in terms of macroscopic quantities. This result allows the ..."
Abstract
 Add to MetaCart
This paper extends recent results in the GP schema theory by formulating a proper exact schema theorem for GP with onepoint crossover. This gives an exact expression for the expected number of instances of a schema at the next generation in terms of macroscopic quantities. This result allows
Exact schema theorem and effective fitness for GP with onepoint crossover
 Proceedings of the Genetic and Evolutionary Computation Conference, pages 469476, Las Vegas
, 2000
"... This paper extends recent results in the GP schema theory by formulating a proper exact schema theorem for GP with onepoint crossover. This gives an exact expression for the expected number of instances of a schema at the next generation in terms of macroscopic quantities. This result allows the ex ..."
Abstract

Cited by 32 (17 self)
 Add to MetaCart
This paper extends recent results in the GP schema theory by formulating a proper exact schema theorem for GP with onepoint crossover. This gives an exact expression for the expected number of instances of a schema at the next generation in terms of macroscopic quantities. This result allows
Microscopic and Macroscopic Schema Theories for Genetic Programming and Variablelength Genetic Algorithms with OnePoint Crossover, their Use and their Relations with Earlier GP and GA Schema Theories
, 2000
"... A few schema theorems for GP have been proposed in the literature in the last few years. One of their main weaknesses is that they provide only a lower bound for the expected value of the number of instances of a given schema H at the next generation, E[m(H; t + 1)], rather than an exact value. Th ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
A few schema theorems for GP have been proposed in the literature in the last few years. One of their main weaknesses is that they provide only a lower bound for the expected value of the number of instances of a given schema H at the next generation, E[m(H; t + 1)], rather than an exact value
Results 1  10
of
471