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Christopher R. Houck

Bio: Christopher R. Houck is an academic researcher from North Carolina State University. The author has contributed to research in topics: Genetic algorithm & Metaheuristic. The author has an hindex of 5, co-authored 6 publications receiving 2341 citations.

Papers
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01 Jan 2001
TL;DR: The genetic algorithm using a oat representation is found to be superior to both a binary genetic algorithm and simulated annealing in terms of e ciency and quality of solution.
Abstract: A genetic algorithm implemented in Matlab is presented. Matlab is used for the following reasons: it provides many built in auxiliary functions useful for function optimization; it is completely portable; and it is e cient for numerical computations. The genetic algorithm toolbox developed is tested on a series of non-linear, multi-modal, non-convex test problems and compared with results using simulated annealing. The genetic algorithm using a oat representation is found to be superior to both a binary genetic algorithm and simulated annealing in terms of e ciency and quality of solution. The use of genetic algorithm toolbox as well as the code is introduced in the paper.

1,318 citations

Proceedings ArticleDOI
27 Jun 1994
TL;DR: This paper discusses the use of non-stationary penalty functions to solve general nonlinear programming problems (NP) using real-valued GAs and the effectiveness of these methods is reported.
Abstract: We discuss the use of non-stationary penalty functions to solve general nonlinear programming problems (NP) using real-valued GAs. The non-stationary penalty is a function of the generation number; as the number of generations increases so does the penalty. Therefore, as the penalty increases it puts more and more selective pressure on the GA to find a feasible solution. The ideas presented in this paper come from two basic areas: calculus-based nonlinear programming and simulated annealing. The non-stationary penalty methods are tested on four NP test cases and the effectiveness of these methods are reported. >

781 citations

Journal ArticleDOI
TL;DR: This paper examines the application of a genetic algorithm used in conjunction with a local improvement procedure for solving the location-allocation problem, a traditional multifacility location problem, and demonstrated that the genetic algorithm provides the best solutions.

179 citations

Journal ArticleDOI
TL;DR: This paper examines the issue of using partial Lamarckianism (i.e., the updating of the genetic representation for only a percentage of the individuals), as compared to pure Lamarckia and pure Baldwinian learning in hybrid GAs.
Abstract: Genetic algorithms (GAs) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region in which the algorithm converges. Hybrid GAs are the combination of improvement procedures, which are good at finding local optima, and GAs. There are two basic strategies for using hybrid GAs. In the first, Lamarckian learning, the genetic representation is updated to match the solution found by the improvement procedure. In the second, Baldwinian learning, improvement procedures are used to change the fitness landscape, but the solution that is found is not encoded back into the genetic string. This paper examines the issue of using partial Lamarckianism (i.e., the updating of the genetic representation for only a percentage of the individuals), as compared to pure Lamarckian and pure Baldwinian learning in hybrid GAs. Multiple instances of five bounded nonlinear problems, the location-allocation problem, and the cell formation problem were used as test problems in an empirical investigation. Neither a pure Lamarckian nor a pure Baldwinian search strategy was found to consistently lead to quicker convergence of the GA to the best known solution for the series of test problems. Based on a minimax criterion (i.e., minimizing the worst case performance across all test problem instances), the 20% and 40% partial Lamarckianism search strategies yielded the best mixture of solution quality and computational efficiency.

110 citations

01 Jan 2007
TL;DR: In the empirical investigation conducted, a general trend was observed where increasing use of Lamarckian learning led to the quicker convergence of the genetic algorithm to the best known solution for a series of test problems.
Abstract: Genetic algorithms(GA) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region at which the algorithm converges. Hybrid genetic algorithms are the combination of improvement procedures, usually working as evaluation functions, and genetic algorithms. There are two basic strategies in using hybrid GAs, Lamarckian and Baldwinian learning. Traditional schema theory does not support Lamarckian learning, i.e., forcing the genetic representation to match the solution found by the improvement procedure. However, Lamarckian learning does alleviate the problem of multiple genotypes mapping to the same phenotype. Baldwinian learning uses improvement procedures to change the fitness landscape, but the solution that is found is not encoded back into the genetic string. This paper empirically examines the issues of using Lamarckian and Baldwinian learning in hybrid GAs. In the empirical investigation conducted, a general trend was observed where increasing use of Lamarckian learning led to the quicker convergence of the genetic algorithm to the best known solution for a series of test problems.

26 citations


Cited by
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Journal ArticleDOI
TL;DR: GA's population-based approach and ability to make pair-wise comparison in tournament selection operator are exploited to devise a penalty function approach that does not require any penalty parameter to guide the search towards the constrained optimum.

3,495 citations

Book
01 Jan 2004
TL;DR: In this article, the authors present a set of heuristics for solving problems with probability and statistics, including the Traveling Salesman Problem and the Problem of Who Owns the Zebra.
Abstract: I What Are the Ages of My Three Sons?.- 1 Why Are Some Problems Difficult to Solve?.- II How Important Is a Model?.- 2 Basic Concepts.- III What Are the Prices in 7-11?.- 3 Traditional Methods - Part 1.- IV What Are the Numbers?.- 4 Traditional Methods - Part 2.- V What's the Color of the Bear?.- 5 Escaping Local Optima.- VI How Good Is Your Intuition?.- 6 An Evolutionary Approach.- VII One of These Things Is Not Like the Others.- 7 Designing Evolutionary Algorithms.- VIII What Is the Shortest Way?.- 8 The Traveling Salesman Problem.- IX Who Owns the Zebra?.- 9 Constraint-Handling Techniques.- X Can You Tune to the Problem?.- 10 Tuning the Algorithm to the Problem.- XI Can You Mate in Two Moves?.- 11 Time-Varying Environments and Noise.- XII Day of the Week of January 1st.- 12 Neural Networks.- XIII What Was the Length of the Rope?.- 13 Fuzzy Systems.- XIV Everything Depends on Something Else.- 14 Coevolutionary Systems.- XV Who's Taller?.- 15 Multicriteria Decision-Making.- XVI Do You Like Simple Solutions?.- 16 Hybrid Systems.- 17 Summary.- Appendix A: Probability and Statistics.- A.1 Basic concepts of probability.- A.2 Random variables.- A.2.1 Discrete random variables.- A.2.2 Continuous random variables.- A.3 Descriptive statistics of random variables.- A.4 Limit theorems and inequalities.- A.5 Adding random variables.- A.6 Generating random numbers on a computer.- A.7 Estimation.- A.8 Statistical hypothesis testing.- A.9 Linear regression.- A.10 Summary.- Appendix B: Problems and Projects.- B.1 Trying some practical problems.- B.2 Reporting computational experiments with heuristic methods.- References.

2,089 citations

Journal ArticleDOI
TL;DR: A comprehensive survey of the most popular constraint-handling techniques currently used with evolutionary algorithms, including approaches that go from simple variations of a penalty function, to others, more sophisticated, that are biologically inspired on emulations of the immune system, culture or ant colonies.

1,924 citations

Journal ArticleDOI
TL;DR: The bias-variance decomposition of the error is provided in this paper, which shows that the success of GASEN may lie in that it can significantly reduce the bias as well as the variance.

1,898 citations

Journal ArticleDOI
TL;DR: This paper revision the terminology, which is unclear and confusing, thereby providing a classification of such control mechanisms, and surveys various forms of control which have been studied by the evolutionary computation community in recent years.
Abstract: The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and promising areas of research in evolutionary computation: it has a potential of adjusting the algorithm to the problem while solving the problem. In the paper we: 1) revise the terminology, which is unclear and confusing, thereby providing a classification of such control mechanisms, and 2) survey various forms of control which have been studied by the evolutionary computation community in recent years. Our classification covers the major forms of parameter control in evolutionary computation and suggests some directions for further research.

1,742 citations