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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 ..."
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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 algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.
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 ..."
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Cited by 97 (3 self)
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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 implications for how well a GA is performing. Interpretations of the Schema Theorem have implicitly assumed that a correlation exists between parent and offspring fitnesses, and this assumption is made explicit in results based on Price's Covariance and Selection Theorem. Schemata do not play a part in the performance theorems derived for representations and operators in general. However, schemata reemerge when recombination operators are used. Using Geiringer's recombination distribution representation of recombination operators, a "missing" schema theorem is derived which makes explicit the intuition for when a GA should perform well. Finally, the method of "adaptive landscape" analysis is exa...
SearchIntensive Concept Induction
, 1995
"... This paper describes REGAL, a distributed genetic algorithmbased system, designed for learning First Order Logic concept descriptions from examples. The system is a hybrid between the Pittsburgh and the Michigan approaches, as the population constitutes a redundant set of partial concept descriptio ..."
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Cited by 90 (3 self)
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This paper describes REGAL, a distributed genetic algorithmbased system, designed for learning First Order Logic concept descriptions from examples. The system is a hybrid between the Pittsburgh and the Michigan approaches, as the population constitutes a redundant set of partial concept descriptions, each evolved separately. In order to increase effectiveness, REGAL is specifically tailored to the concept learning task; hence, REGAL is taskdependent, but, on the other hand, domainindependent. The system proved to be particularly robust with respect to parameter setting across a variety of different application domains. REGAL is based on a selection operator, called Universal Suffrage operator, provably allowing the population to asymptotically converge, in average, to an equilibrium state, in which several species coexist. The system is presented both in a serial and in a parallel version, and a new distributed computational model is proposed and discussed. The system has been test...
Lamarckian Evolution, The Baldwin Effect and Function Optimization
, 1994
"... We compare two forms of hybrid genetic search. The first uses Lamarckian evolution, while the second uses a related method where local search is employed to change the fitness of strings, but the acquired improvements do not change the genetic encoding of the individual. ..."
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Cited by 79 (1 self)
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We compare two forms of hybrid genetic search. The first uses Lamarckian evolution, while the second uses a related method where local search is employed to change the fitness of strings, but the acquired improvements do not change the genetic encoding of the individual.
The Algebra of Genetic Algorithms
, 1994
"... A rigorous formulation of the generalisation of schema analysis known as forma analysis is presented. This is shown to provide a direct mechanism for harnessing knowledge about a search space, codified through the imposition of equivalence relations over that space, to generate a genetic represen ..."
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Cited by 70 (11 self)
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A rigorous formulation of the generalisation of schema analysis known as forma analysis is presented. This is shown to provide a direct mechanism for harnessing knowledge about a search space, codified through the imposition of equivalence relations over that space, to generate a genetic representation and operators. It is shown that a single characterisation of a space leads to a unique genetic representation, and the kinds of representations that are possible are classified and discussed. A relatively new operator, call random assorting recombination (RARw ), is defined rigorously and is shown to be, in an important sense, a universal recombination operator.
The distributed genetic algorithm revisited
 Proceedings of the Sixth International Conference on Genetic Algorithms
, 1995
"... This paper extends previous work done by Tanese on the distributed genetic algorithm (DGA). Tanese found that the DGA outperformed the canonical serial genetic algorithm (CGA) on a class of di cult, randomlygenerated Walsh polynomials. This left open the question of whether the DGA would have simila ..."
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Cited by 68 (0 self)
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This paper extends previous work done by Tanese on the distributed genetic algorithm (DGA). Tanese found that the DGA outperformed the canonical serial genetic algorithm (CGA) on a class of di cult, randomlygenerated Walsh polynomials. This left open the question of whether the DGA would have similar success on functions that were more amenable to optimization by the CGA. In this work, experiments were done to compare the DGA's performance on the Royal Road class of tness functions to that of the CGA. Besides achieving superlinear speedup on KSR parallel computers, the DGA again outperformed the CGA on the functions R3 and R4 with regard to the metrics of best tness, average tness, and number of times the optimum was reached. Its performance on 1 and 2 was comparable to that of the CGA. The e ect of varying the DGA's migration parameters was also investigated. The results of the experiments are presented and discussed, and suggestions for future research are made. R R i
Analysis of Complexity Drift in Genetic Programming
 Genetic Programming 1997: Proceedings of the Second Annual Conference
, 1997
"... One serious problem of standard Genetic Programming (GP) is that evolved structures appear to drift towards large and slow forms on average. This paper presents a novel analysis of the role played by variable complexity in the selection and survival of GP expressions. It defines a particular propert ..."
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Cited by 67 (1 self)
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One serious problem of standard Genetic Programming (GP) is that evolved structures appear to drift towards large and slow forms on average. This paper presents a novel analysis of the role played by variable complexity in the selection and survival of GP expressions. It defines a particular property of GP representations, called rooted treeschema, that sheds light on the role of variable complexity of evolved structures. A rooted treeschema is a relation on the space of treeshaped structures which provides a quantifiable partitioning of the search space. The paper analyzes the influence of parsimony pressure on selection and growth of structures. Experimental evidence confirms theoretical predictions. 1 Introduction Genetic programming (GP) uses openended complexity representations of flexible semantics (Koza1992). GP evolves a population of expressions in some problem dependent language that encode problem solutions. Evolved expressions are tree structured and can be interpret...
Statistical dynamics of the Royal Road genetic algorithm
 Theoretical Computer Science
, 1999
"... Metastability is a common phenomenon. Many evolutionary processes, both natural and artificial, alternate between periods of stasis and brief periods of rapid change in their behavior. In this paper an analytical model for the dynamics of a mutationonly genetic algorithm (GA) is introduced that iden ..."
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Cited by 62 (5 self)
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Metastability is a common phenomenon. Many evolutionary processes, both natural and artificial, alternate between periods of stasis and brief periods of rapid change in their behavior. In this paper an analytical model for the dynamics of a mutationonly genetic algorithm (GA) is introduced that identifies a new and general mechanism causing metastability in evolutionary dynamics. The GA’s population dynamics is described in terms of flows in the space of fitness distributions. The trajectories through fitness distribution space are derived in closed form in the limit of infinite populations. We then show how finite populations induce metastability, even in regions where fitness does not exhibit a local optimum. In particular, the model predicts the occurrence of “fitness epochs”—periods of stasis in population fitness distributions—at finite population size and identifies the locations of these fitness epochs with the flow’s hyperbolic fixed points. This enables exact predictions of the metastable fitness distributions during the fitness epochs, as well as giving insight into the nature of the periods of stasis and the innovations between them. All these results are obtained as closedform expressions in terms of the GA’s parameters.
Finite Markov Chain Results in Evolutionary Computation: A Tour d'Horizon
, 1998
"... . The theory of evolutionary computation has been enhanced rapidly during the last decade. This survey is the attempt to summarize the results regarding the limit and finite time behavior of evolutionary algorithms with finite search spaces and discrete time scale. Results on evolutionary algorithms ..."
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Cited by 61 (2 self)
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. The theory of evolutionary computation has been enhanced rapidly during the last decade. This survey is the attempt to summarize the results regarding the limit and finite time behavior of evolutionary algorithms with finite search spaces and discrete time scale. Results on evolutionary algorithms beyond finite space and discrete time are also presented but with reduced elaboration. Keywords: evolutionary algorithms, limit behavior, finite time behavior 1. Introduction The field of evolutionary computation is mainly engaged in the development of optimization algorithms which design is inspired by principles of natural evolution. In most cases, the optimization task is of the following type: Find an element x 2 X such that f(x ) f(x) for all x 2 X , where f : X ! IR is the objective function to be maximized and X the search set. In the terminology of evolutionary computation, an individual is represented by an element of the Cartesian product X \Theta A, where A is a possibly...
The Island Model Genetic Algorithm: On Separability, Population Size and Convergence
 Journal of Computing and Information Technology
, 1998
"... Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model genetic algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic di ..."
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Cited by 40 (0 self)
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Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model genetic algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic diversity, since each island can potentially follow a different search trajectory through the search space. It is also possible that since linearly separable problems are often used to test Genetic Algorithms, that Island Models may simply be particularly well suited to exploiting the separable nature of the test problems. We explore this possibility by using the infinite population models of simple genetic algorithms to study how Island Models can track multiple search trajectories. We also introduce a simple model for better understanding when Island Model genetic algorithms may have an advantage when processing some test problems. We provide empirical results for both linearly separa...