A hybrid evolutionary approach for solving constrained optimization problems over finite domains

A novel approach for the integration of evolution programs and constraint-solving techniques over finite domains is presented. This integration provides a problem-independent optimization strategy for large-scale constrained optimization problems over finite domains. In this approach, genetic operat...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 4; no. 4; pp. 353 - 372
Main Authors: Ruiz-Andino, A., Araujo, L., Saenz, F., Ruz, J.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.11.2000
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied sciences
Biological cells
Chromosomes
Computer languages
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Constraint optimization
Constraints
Data structures
Design optimization
Evolutionary computation
Exact sciences and technology
Flows in networks. Combinatorial problems
Genetics
Large scale integration
Mathematical analysis
Memory architecture
Operational research and scientific management
Operational research. Management science
Operators
Optimization
Searching
Software
Studies
Testing
Programming language
Parallel system
Genetic algorithm
Evolutionary algorithm
Consistency
Problem solving
Constraint satisfaction
Large scale
Hybridization
Shared memory
Parallelization
Constrained optimization
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Description
Summary:A novel approach for the integration of evolution programs and constraint-solving techniques over finite domains is presented. This integration provides a problem-independent optimization strategy for large-scale constrained optimization problems over finite domains. In this approach, genetic operators are based on an arc-consistency algorithm, and chromosomes are arc-consistent portions of the search space of the problem. The paper describes the main issues arising in this integration: chromosome representation and evaluation, selection and replacement strategies, and the design of genetic operators. We also present a parallel execution model for a distributed memory architecture of the previous integration. We have adopted a global parallelization approach that preserves the properties, behavior, and fundamentals of the sequential algorithm. Linear speedup is achieved since genetic operators are coarse grained as they perform a search in a discrete space carrying out arc consistency. The implementation has been tested on a GRAY T3E multiprocessor using a complex constrained optimization problem.
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ISSN:1089-778X
1941-0026
DOI:10.1109/4235.887235