A Practical Schema Theorem For Genetic Algorithm Design and Tuning
Goldberg, D.E., Sastry, K. (2001)TR No.: 2001017 | Download PDF | Download PS
Abstract:
This paper develops the theory that can enable the design of genetic
algorithms and choose the parameters such that the proportion of the best
building blocks grow. A practical schema theorem has been used for this
purpose and its ramification for the choice of selection operator and
parameterization of the algorithm is explored. In particular stochastic
universal selection, tournament selection, […]
Posted: March 8th, 2001 under Genetic algorithms. Comments: none
Cluster Optimization Using Extended Compact Genetic Algorithm
Sastry, K., Xiao, G. (2001)TR No.: 2001016 | Download PDF | Download PS
Abstract:
This paper presents an efficient cluster optimization algorithm. The
proposed algorithm uses extended compact genetic algorithm (ECGA), one of
the competent genetic algorithms (GAs) coupled with Nelder-Mead simplex
local search. The lowest energy structures of silicon clusters with 4-11
atoms have been successfully predicted. The minimum population size and
total number of function (potential energy of the cluster) evaluations
required to […]
Posted: March 4th, 2001 under Genetic algorithms. Comments: none
On the Supply of Building Blocks
Goldberg, D.E., Sastry, K., Latoza, T. (2001)TR No.: 2001015 | Download PDF | Download PS
Abstract:
This study addresses the issue of building block supply in the initial
population. Facetwise models for supply of a single building block as well
as for supply of all schemata in a partition have been developed. An
estimate for the population size required to ensure the presence of all
raw building blocks has been derived using these facetwise models. […]
Posted: February 24th, 2001 under Genetic algorithms. Comments: none
From TwoMax to the Ising Model: Easy and hard symmetrical problems
Van Hoyweghen, C., Goldberg, D.E., Naudts, B. (2001)TR No.: 2001030 | Download PDF | Download PS
Abstract:
The paper shows that there is a key dividing line between two types of
symmetrical problems: problems for which a genetic algorithm (GA) benefits from the fact that
genetic drift chooses between equally good partial solutions, and problems for
which all equally good partial solutions have to be preserved to find an
optimum. By analyzing in detail the search […]
Posted: February 20th, 2001 under Genetic algorithms. Comments: none
Modeling Tournament With Replacement Using Apparent Added Noise
Sastry, K., Goldberg, D.E. (2001)TR No.: 2001014 | Download PDF | Download PS
Abstract:
This paper analyzes the effects of tournament selection with replacement
on the convergence time and population sizing for selectorecombinative
genetic algorithms. This paper empirically demonstrates that the run
duration remains the same and is not affected whether the tournament
selection is performed with or without replacement. However, the
population size required is more if tournament selection is performed with
replacement rather […]
Posted: February 20th, 2001 under Genetic algorithms. Comments: none
Evolutionary Algorithms + Graphical Models = Scalable Black-Box Optimization
Pelikan, M., Sastry, K., Goldberg, D.E. (2001)TR No.: 2001029 | Download PDF | Download PS
Abstract:
To solve a wide range of different problems, the research in black-box optimization faces several important challenges. One of the most important challenges is the design of methods capable of automatically discovering the regularities in the problem and utilizing these to ensure efficient and reliable search. This paper discusses the Bayesian optimization algorithm (BOA) that […]
Posted: February 16th, 2001 under Genetic algorithms. Comments: none
Don’t Evaluate, Inherit
Sastry, K., Goldberg, D.E., Pelikan, M. (2001)TR No.: 2001013 | Download PDF | Download PS
Abstract:
This paper studies fitness inheritance as an efficiency enhancement
technique for genetic and evolutionary algorithms. Convergence and
population sizing models are derived and compared with experimental
results. These models are optimized for greatest speed-up and the optimal
inheritance proportion to obtain such a speed-up is derived. Results also
show that when the inheritance effects are considered in the population
sizing model, […]
Posted: February 16th, 2001 under Genetic algorithms. Comments: none
Convergence-time models for the simple genetic algorithm with finite population
Ceroni, A., Pelikan, M., Goldberg, D.E. (2001)TR No.: 2001028 | Download PDF | Download PS
Abstract:
This paper presents various convergence models for the simple genetic algorithm (SGA) in the case of finite population. A piecewise convergence-time model is derived using ideas from two existing convergence models. The factors affecting the convergence with small population size are explained and used to construct a correct model of the variance in fitness for […]
Posted: February 12th, 2001 under Genetic algorithms. Comments: none
Verification of the Theory of Genetic and Evolutionary Continuation
Srivastava, R.P., Goldberg, D.E. (2001)TR No.: 2001007 | Download PDF | Download PS
Abstract:
This paper makes a first attempt to study and verify empirically the
theory of proposed continuation operators through systematic formulation
of experiments. Both the basic, and in a sense bounding, cases of
building block salience, as encountered in difficult problems, are dealt
with individually. Experimental results closely match theory and assure
us of the usefulness of an apt blend […]
Posted: February 12th, 2001 under Genetic algorithms. Comments: none
Classifiers that Approximate Functions
Wilson, S.W. (2001)TR No.: 2001027 | Download PDF | Download PS
Abstract:
A classifier system, XCSF, is introduced in which the prediction
estimation mechanism is used to learn approximations to functions.
The addition of weight vectors to the classifiers allows
piecewise-linear approximation, where the classifier’s
prediction is calculated instead of being a fixed scalar. Results
on functions of up to six dimensions show high accuracy.
The idea of calculating the prediction leads […]
Posted: February 8th, 2001 under Genetic algorithms. Comments: none