Results 1  10
of
170
The sequence of the human genome
, 2001
"... The following resources related to this article are available online at ..."
Abstract

Cited by 511 (6 self)
 Add to MetaCart
(Show Context)
The following resources related to this article are available online at
Predictive Models for the Breeder Genetic Algorithm  I. Continuous Parameter Optimization
 EVOLUTIONARY COMPUTATION
, 1993
"... In this paper a new genetic algorithm called the Breeder Genetic Algorithm (BGA) is introduced. The BGA is based on artificial selection similar to that used by human breeders. A predictive model for the BGA is presented which is derived from quantitative genetics. The model is used to predict t ..."
Abstract

Cited by 360 (25 self)
 Add to MetaCart
In this paper a new genetic algorithm called the Breeder Genetic Algorithm (BGA) is introduced. The BGA is based on artificial selection similar to that used by human breeders. A predictive model for the BGA is presented which is derived from quantitative genetics. The model is used to predict the behavior of the BGA for simple test functions. Different mutation schemes are compared by computing the expected progress to the solution. The numerical performance of the BGA is demonstrated on a test suite of multimodal functions. The number of function evaluations needed to locate the optimum scales only as n ln(n) where n is the number of parameters. Results up to n = 1000 are reported.
A Comparison of Selection Schemes used in Genetic Algorithms
 Gloriastrasse 35, CH8092 Zurich: Swiss Federal Institute of Technology (ETH) Zurich, Computer Engineering and Communications Networks Lab (TIK
, 1995
"... Genetic Algorithms are a common probabilistic optimization method based on the model of natural evolution. One important operator in these algorithms is the selection scheme for which a new description model is introduced in this paper. With this a mathematical analysis of tournament selection, trun ..."
Abstract

Cited by 83 (3 self)
 Add to MetaCart
Genetic Algorithms are a common probabilistic optimization method based on the model of natural evolution. One important operator in these algorithms is the selection scheme for which a new description model is introduced in this paper. With this a mathematical analysis of tournament selection, truncation selection, linear and exponential ranking selection and proportional selection is carried out that allows an exact prediction of the fitness values after selection. The further analysis derives the selection intensity, selection variance, and the loss of diversity for all selection schemes. For completion a pseudocode formulation of each method is included. The selection schemes are compared and evaluated according to their properties leading to an unified view of these different selection schemes. Furthermore the correspondence of binary tournament selection and ranking selection in the expected fitness distribution is proven. Foreword This paper is the revised and extended versio...
A Comparison of Selection Schemes used in Evolutionary Algorithms
 Evolutionary Computation
, 1997
"... Evolutionary Algorithms are a common probabilistic optimization method based on the model of natural evolution. One important operator in these algorithms is the selection scheme for which in this paper a new description model based on fitness distributions is introduced. ..."
Abstract

Cited by 68 (2 self)
 Add to MetaCart
Evolutionary Algorithms are a common probabilistic optimization method based on the model of natural evolution. One important operator in these algorithms is the selection scheme for which in this paper a new description model based on fitness distributions is introduced.
Replicator Equations, Maximal Cliques, and Graph Isomorphism
, 1999
"... We present a new energyminimization framework for the graph isomorphism problem that is based on an equivalent maximum clique formulation. The approach is centered around a fundamental result proved by Motzkin and Straus in the mid1960s, and recently expanded in various ways, which allows us to fo ..."
Abstract

Cited by 54 (11 self)
 Add to MetaCart
(Show Context)
We present a new energyminimization framework for the graph isomorphism problem that is based on an equivalent maximum clique formulation. The approach is centered around a fundamental result proved by Motzkin and Straus in the mid1960s, and recently expanded in various ways, which allows us to formulate the maximum clique problem in terms of a standard quadratic program. The attractive feature of this formulation is that a clear onetoone correspondence exists between the solutions of the quadratic program and those in the original, combinatorial problem. To solve the program we use the socalled replicator equations—a class of straightforward continuous and discretetime dynamical systems developed in various branches of theoretical biology. We show how, despite their inherent inability to escape from local solutions, they nevertheless provide experimental results that are competitive with those obtained using more elaborate meanfield annealing heuristics.
Domino Convergence, Drift, and the TemporalSalience Structure of Problems
, 1998
"... The convergence speed of building blocks depends on their marginal fitness contribution or on the salience structure of the problem. We use a sequential parameterization approach to build models of the differential convergence behavior, and derive time complexities for the boundary case which is obt ..."
Abstract

Cited by 50 (17 self)
 Add to MetaCart
The convergence speed of building blocks depends on their marginal fitness contribution or on the salience structure of the problem. We use a sequential parameterization approach to build models of the differential convergence behavior, and derive time complexities for the boundary case which is obtained with an exponentially scaled problem (BinInt). We show that this domino convergence time complexity is linear in the number of building blocks (O(l)) for selection algorithms with constant selection intensity (such as tournament selection and ( ; ) or truncation selection), and exponential (O(2 l )) for proportionate selection. These complexities should be compared with the convergence speed for uniformly salient problems which are respectively (O( p l)) and (O(l ln l)). In addition we relate this facetwise model to a genetic drift model, and identify where and when the stochastic uctuations due to drift overwhelms the domino convergence, resulting in drift stall. The combined mo...
Ruggedness and Neutrality  The NKp family of Fitness Landscapes
 Alive VI: Sixth International Conference on Articial Life
, 1998
"... It has come to be almost an article of faith amongst population biologists and GA researchers alike that the principal feature of a fitness landscape as regards evolutionary dynamics is "ruggedness", particularly as measured by the autocorrelation function. In this paper we demonstrate th ..."
Abstract

Cited by 48 (2 self)
 Add to MetaCart
(Show Context)
It has come to be almost an article of faith amongst population biologists and GA researchers alike that the principal feature of a fitness landscape as regards evolutionary dynamics is "ruggedness", particularly as measured by the autocorrelation function. In this paper we demonstrate that autocorrelation alone may be inadequate as a mediator of evolutionary dynamics, specifically in the presence of large scale neutrality. We introduce the NKp family of landscapes (a variant on NK landscapes) which possess the remarkable property that varying the degree of neutrality has minimal effect on the correlation structure. It is demonstrated that NKp landscapes feature neutral networks which have a "constant innovation" property comparable with the neutral networks observed in models of RNA secondary structure folding landscapes. We show that evolutionary dynamics on NKp landscapes vary dramatically with the degree of neutrality  at high neutrality the dynamics are characterised by populat...
TANGLED WEBS  Evolutionary Dynamics on Fitness Landscapes with Neutrality
, 1997
"... The bulk of research on the dynamics of populations of genotypes evolving on fitness landscapes has concentrated on the rôle of correlation and landscape ruggedness as a putative indicator of the qual... ..."
Abstract

Cited by 40 (3 self)
 Add to MetaCart
The bulk of research on the dynamics of populations of genotypes evolving on fitness landscapes has concentrated on the rôle of correlation and landscape ruggedness as a putative indicator of the qual...
On the mean convergence time of evolutionary algorithms without selection and mutation
 PARALLEL PROBLEM SOLVING FROM NATURE, LECTURE NOTES IN COMPUTER SCIENCE 866
, 1994
"... In this paper we study random genetic drift in a finite genetic population. Exact formulae for calculating the mean convergence time of the population are analytically derived and some results of numerical calculations are given. The calculations are compared to the results obtained in population ge ..."
Abstract

Cited by 39 (4 self)
 Add to MetaCart
(Show Context)
In this paper we study random genetic drift in a finite genetic population. Exact formulae for calculating the mean convergence time of the population are analytically derived and some results of numerical calculations are given. The calculations are compared to the results obtained in population genetics. A new proposition is derived for binary alleles and uniform crossover. Here the mean convergence time is almost proportional to the size of the population and to the logarithm of the number of the loci. The results of Monte Carlo type numerical simulations are in agreement with the results from the calculation.
Genetic Algorithms
, 1997
"... this paper. Bremermann's algorithm contained most of the ingredients of a good evolutionary algorithm. But because of limited computer experiments and a missing theory, he did not find a good combination of the ingredients. In the 70's two different evolutionary algorithms independently e ..."
Abstract

Cited by 35 (0 self)
 Add to MetaCart
this paper. Bremermann's algorithm contained most of the ingredients of a good evolutionary algorithm. But because of limited computer experiments and a missing theory, he did not find a good combination of the ingredients. In the 70's two different evolutionary algorithms independently emerged  the genetic algorithm GA of Holland [1975] and the evolution strategies of Rechenberg [1973] and Schwefel [1981] . Holland was not so much interested in optimization, but in adaptation. He investigated the genetic algorithm with decision theory for discrete domains. Holland emphasized the importance of recombination in large populations, whereas Rechenberg and Schwefel mainly investigated mutation in very small populations for continuous parameter optimization.