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...

This paper describes a genetically guided approach to optimizing the hard (J1) and fuzzy (Jm) c-means functionals used in cluster analysis. Our experiments show that a genetic algorithm ameliorates the difficulty of choosing an initialization for the c-means clustering algorithms. Experiments use six data sets, including the Iris data, magnetic resonance and color images. The genetic algorithm approach is generally able to find the lowest known Jm value or a Jm associated with a partition very similar to that associated with the lowest Jm value. On data sets with several local extrema, the GA approach always avoids the less desirable solutions. Degenerate partitions are always avoided by the GA approach, which provides an effiective method for optimizing clustering models whose objective function can be represented in terms of cluster centers. The time cost of genetic guided clustering is shown to make a series random initializations of fuzzy/hard c-means, where the partition a...

Abstract � Adaptation of parameters and operators is one of the most important and promising areas of research in evolutionary computation � it tunes the algorithm to the problem while solving the problem. In this paper we develop a classi�cation of adaptation on the basis of the mechanisms used � and the level at which adaptation operates within the evolutionary algorithm. The classi�cation covers all forms of adaptation in evolutionary computation and suggests fur� ther research. I.

Given a parallel program represented by a task graph, the objective of a scheduling algorithm is to minimize the overall execution time of the program by properly assigning the nodes of the graph to the processors. This multiprocessor scheduling problem is NP-complete even with simplifying assumptions, and becomes more complex under relaxed assumptions such as arbitrary precedence constraints, and arbitrary task execution and communication times. The present literature on this topic is a large repertoire of heuristics that produce good solutions in a reasonable amount of time. These heuristics, however, have restricted applicability in a practical environment because they have a number of fundamental problems including high time complexity, lack of scalability, and no performance guarantee with respect to optimal solutions. Recently, genetic algorithms (GAs) have been widely reckoned as a useful vehicle for obtaining high quality or even optimal solutions for a broad range of combinato...

. The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms have been built for inducing exploitation/exploration relationships that avoid the premature convergence problem and improve the final results. One of the most widely studied adaptive approaches are the adaptive parameter setting techniques. In this paper, we study these techniques in depth, based on the use of fuzzy logic controllers. Furthermore, we design and discuss an adaptive realcoded genetic algorithm based on the use of fuzzy logic controllers. Although suitable results have been obtained by using this type of adaptive technique, we report some reflections on open problems that still remain. Keywords. Exploitation/exploration relationship, adaptive genetic algorithms, fuzzy logic controllers. 1 Introduction GA behaviour is strongly determined by the balance between exploiting what already works best and exploring possibilities t...

Thi paper presents ahybri geneti algorib2 (GA) for thebi;[663b2DW publi transportdrisp scheduliD problem. A greedy heurib[; i used, whi, constructs a schedule bysequentiD6W selectii shicti from a very large set of pregenerated legalpotentiB shinti to cover theremai6DD work.Indi;[;61 shii and the schedule as a whole have to be evaluatedi the process. Fuzzy set theoryi applib on suchevaluatib2D For ibDD3[3b2 shiDD3 thei structurale#cictu i assessed by fuzzib2 criib2 iiib2D frompracti9; knowledge of the problem domaim A GAi s used toderi[ a near-optiD1 wei-o di-optiD19 amongst thefuzzi63 crii63D so that asi6V;b2D;D6B weibib evaluati; can be computed for eachshibV Thecorrespondi; schedule constructedutistruc theweiD1 diD136Vb2DV i evaluated by the GA#s fitnessfuncti3D i whii the two objectib2 ofmi1WW3b2D the number ofshi13 andmiD[36Vb2 the total cost are formulated as a fuzzy goal.Comparatib results on real-world problems are presented.

Parallel Genetic Algorithms (PGA) offer a natural and productive way to solve a problem better than a single population. Up to now the different PGA paradigms use the same evolution behaviour on each population. This paper proposes a method, called Co-operating Populations with Different Evolution Behaviours (CoPDEB) where the populations are allowed to exhibit different evolution behaviours. This is achieved by using a variety of selection mechanisms, operators, communication methods, and parameters as it is explained in the sequel. This method has been tested on the problem of training a Recurrent Artificial Neural Network (RANN).

In this paper, a concept of degree of population diversity is introduced to quantitatively characterize and theoretically analyze the problem of premature convergence in genetic algorithms (GAs) within the framework of Markov chain. Under the assumption that the mutation probability is zero, the search ability of the GAs is discussed. It is proved that the degree of population diversity converges to zero with probability 1 so that the search ability of a genetic algorithm (GA) decreases and premature convergence occurs. Moreover, an explicit formula for the conditional probability of allele loss at a certain bit position is established to show the relationships between premature convergence and the GA parameters, such as population size, mutation probability, and some population statistics. The formula also partly answers the questions of to where a GA most likely converges. The theoretical results are all supported by the simulation experiments. Keywords: Genetic algorithms, Prematur...

Abstract—Configuration similarity is a special form of content-based image retrieval that considers relative object locations. It can be used as a standalone method, or to complement retrieval based on visual or semantic features. The corresponding queries ask for sets of objects that satisfy some spatio-temporal constraints, e.g., “find all triplets of objects ( I, P, Q), such that I is northeast of P, which is inside Q. ” Exhaustive processing (i.e., retrieval of the best solutions) of configuration similarity queries, in general, has exponential complexity and fast search for sub-optimal solutions is the only way to deal with the vast amounts of multimedia information in several real-time applications. In this paper we first discuss the utilization of nonsystematic search heuristics, based on genetic algorithms, simulated annealing and hill climbing approaches. An extensive experimentation with real and synthetic datasets reveals that hill climbing techniques are the best for the current problem; therefore, as a subsequent step we study the search space, and develop improved variations of hill climbing that take advantage of the special structure of the problem to enhance speed. The proposed heuristic methods significantly outperform systematic search when there is only limited time for query processing. Index Terms—Content-based retrieval, local search algorithms, spatial similarity. I.

Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of “Natural Selection”. Typically these algorithms maintain a population of individual solutions, each of which has a fitness attached to it, which in some way reflects the quality of the solution. The search proceeds via the iterative generation, evaluation and possible incorporation of new individuals based on the current population, using a number of parameterised genetic operators. In this thesis the phenomenon of Self Adaptation of the genetic operators is investigated. A new framework for classifying adaptive algorithms is proposed, based on the scope of the adaptation, and on the nature of the transition function guiding the search through the space of possible configurations of the algorithm. Mechanisms are investigated for achieving the self adaptation of recombination and mutation operators within a genetic algorithm, and means of combining them are investigated. These are shown to produce significantly better results than any of the combinations of fixed operators tested, across a range of problem types. These new operators reduce the need for the designer of an algorithm to select