Results 1 - 10
of
35
Positive Feedback as a Search Strategy
, 1991
"... : A combination of distributed computation, positive feedback and constructive greedy heuristic is proposed as a new approach to stochastic optimization and problem solving. Positive feedback accounts for rapid discovery of very good solutions, distributed computation avoids premature convergence, a ..."
Abstract
-
Cited by 97 (20 self)
- Add to MetaCart
: A combination of distributed computation, positive feedback and constructive greedy heuristic is proposed as a new approach to stochastic optimization and problem solving. Positive feedback accounts for rapid discovery of very good solutions, distributed computation avoids premature convergence, and greedy heuristic helps the procedure to find acceptable solutions in the early stages of the search process. An application of the proposed methodology to the classical travelling salesman problem shows that the system can rapidly provide very good, if not optimal, solutions. We report on many simulation results and discuss the working of the algorithm. Some hints about how this approach can be applied to a variety of optimization problems are also given. Keywords: Ant Systems, Ant Colonies, Adaptive Systems, Artificial Life, Combinatorial Optimization, Parallel Algorithms. ---ooOoo--- To obtain a copy of this report please fill in your name and address and return this page to: Laboratori...
An Overview of Evolutionary Computation
, 1993
"... Evolutionary computation uses computational models of evolutionary processes as key elements in the design and implementation of computer-based problem solving systems. In this paper we provide an overview of evolutionary computation, and describe several evolutionary algorithms that are current ..."
Abstract
-
Cited by 95 (5 self)
- Add to MetaCart
Evolutionary computation uses computational models of evolutionary processes as key elements in the design and implementation of computer-based problem solving systems. In this paper we provide an overview of evolutionary computation, and describe several evolutionary algorithms that are currently of interest. Important similarities and differences are noted, which lead to a discussion of important issues that need to be resolved, and items for future research.
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
- Artificial Intelligence Review
, 1998
"... . Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of ..."
Abstract
-
Cited by 84 (17 self)
- Add to MetaCart
. Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some me...
An Overview of Genetic Algorithms: Part 1, Fundamentals
, 1993
"... this article may be reproduced for commercial purposes. 1 Introduction ..."
Abstract
-
Cited by 73 (1 self)
- Add to MetaCart
this article may be reproduced for commercial purposes. 1 Introduction
An Indexed Bibliography of Genetic Algorithms in Power Engineering
, 1995
"... s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Ja ..."
Abstract
-
Cited by 67 (8 self)
- Add to MetaCart
s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986 -- Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987 -- 1992 ffl EI M: The Engineering Index Monthly: Jan. 1993 -- Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...
Genetic Set Recombination and its Application to Neural Network Topology Optimisation
- NEURAL COMPUTING AND APPLICATIONS
, 1993
"... Forma analysis is applied to the task of optimising the connectivity of a feed-forward neural network with a single layer of hidden units. This problem is reformulated as a multiset optimisation problem and techniques are developed to allow principled genetic search over fixed- and variable-si ..."
Abstract
-
Cited by 59 (3 self)
- Add to MetaCart
Forma analysis is applied to the task of optimising the connectivity of a feed-forward neural network with a single layer of hidden units. This problem is reformulated as a multiset optimisation problem and techniques are developed to allow principled genetic search over fixed- and variable-size sets and multisets. These techniques require a further generalisation of the notion of gene, which is presented. The result is a non-redundant representation of the neural network topology optimisation problem together with recombination operators which have carefully designed and well-understood properties. The techniques developed have relevance to the application of genetic algorithms to constrained optimisation problems.
Fitness Variance of Formae and Performance Prediction
, 1994
"... Representation is widely recognised as a key determinant of performance in evolutionary computation. The development of families of representation-independentoperators allows the formulation of formal representation-independent evolutionary algorithms. These formal algorithms can be instantiated ..."
Abstract
-
Cited by 58 (7 self)
- Add to MetaCart
Representation is widely recognised as a key determinant of performance in evolutionary computation. The development of families of representation-independentoperators allows the formulation of formal representation-independent evolutionary algorithms. These formal algorithms can be instantiated for particular search problems by selecting a suitable representation. The performance of different representations, in the context of any given formal representation-independent algorithm, can then be measured. Simple analyses suggest that fitness variance of formae (generalised schemata) for the chosen representation might act as a performance predictor for evolutionary algorithms. This hypothesis is tested and supported through studies of four different representations for the travelling sales-rep problem (TSP) in the context of both formal representation-independentgenetic algorithms and corresponding memetic algorithms. 1 Motivation The subject of this paper is representation i...
Ant System: An Autocatalytic Optimizing Process
"... A combination of distributed computation, positive feedback and constructive greedy heuristic is proposed as a new approach to stochastic optimization and problem solving. Positive feedback accounts for rapid discovery of very good solutions, distributed computation avoids premature convergence, and ..."
Abstract
-
Cited by 56 (13 self)
- Add to MetaCart
A combination of distributed computation, positive feedback and constructive greedy heuristic is proposed as a new approach to stochastic optimization and problem solving. Positive feedback accounts for rapid discovery of very good solutions, distributed computation avoids premature convergence, and greedy heuristic helps the procedure to find acceptable solutions in the early stages of the search process. An application of the proposed methodology to the classical travelling salesman problem shows that the system can rapidly provide very good, if not optimal, solutions. We report on many simulation results and discuss the working of the algorithm. Some hints about how this approach can be applied to a variety of optimization problems are also given. 1. Introduction In this paper we explore the emergence of global properties from the interaction of many simple agents. In particular, we are interested in the distribution of search activities over so-called "ants", i.e., agents that use...
Improving Search by Incorporating Evolution Principles in Parallel Tabu Search
- 1994 IEEE Conference on Evolutionary Computation
, 1994
"... Combinatorial optimization problems require computing efforts which grow at least exponentially with the problem dimension. Therefore, the use of the remarkable power of massively parallel systems constitutes an opportunity to be considered for solving significant applications in reasonable times. ..."
Abstract
-
Cited by 14 (0 self)
- Add to MetaCart
Combinatorial optimization problems require computing efforts which grow at least exponentially with the problem dimension. Therefore, the use of the remarkable power of massively parallel systems constitutes an opportunity to be considered for solving significant applications in reasonable times. In this paper, starting from Tabu Search, a general optimization methodology, a parallel version, oriented to distributed memory multiprocessors and including evolution principles, has been introduced and discussed. The experiments have been performed on classical Travelling Salesman Problems and Quadratic Assignment Problems taken from literature. The results obtained show that the incorporation of evolution principles is very fruitful for the search strategy in terms of both convergence speed and solution precision. 1. INTRODUCTION Many optimization techniques have been introduced during last years to find adequate solutions in high dimensional search spaces [1]; among them the mos...

