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Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence

27-29 June 1994

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Displaying Results 1 - 25 of 159
  • Evolving better representations through selective genome growth

    Publication Year: 1994, Page(s):182 - 187 vol.1
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (608 KB)

    The choice of how to represent the search space for a genetic algorithm (GA) is critical to the GA's performance. Representations are usually engineered by hand and fixed for the duration of the GA run. Here a new method is described in which the degrees of freedom of the representation-i.e. the genes-are increased incrementally. The phenotypic effects of the new genes are randomly drawn from a sp... View full abstract»

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  • Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence

    Publication Year: 1994
    Request permission for commercial reuse | PDF file iconPDF (33 KB)
    Freely Available from IEEE
  • Improving search by incorporating evolution principles in parallel Tabu Search

    Publication Year: 1994, Page(s):823 - 828 vol.2
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (432 KB)

    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, ori... View full abstract»

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  • Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways

    Publication Year: 1994, Page(s):312 - 317 vol.1
    Cited by:  Papers (59)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (524 KB)

    Two methods of hybridizing genetic algorithms (GA) with hill-climbing for global optimization are investigated. The first one involves two interwoven levels of optimization-evolution (GA) and individual learning (hill-climbing)-which cooperate in the global optimization process. The second one consists of modifying a GA by the introduction of new genetic operators or by the alteration of tradition... View full abstract»

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  • Fitness landscapes and difficulty in genetic programming

    Publication Year: 1994, Page(s):142 - 147 vol.1
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (528 KB)

    The structure of the fitness landscape on which genetic programming operates is examined. The landscapes of a range of problems of known difficulty are analyzed in an attempt to determine which landscape measures correlate with the difficulty of the problem. The autocorrelation of the fitness values of random walks, a measure which has been shown to be related to perceived difficulty using other t... View full abstract»

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  • Dynamic scheduling of computer tasks using genetic algorithms

    Publication Year: 1994, Page(s):829 - 833 vol.2
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (404 KB)

    We concentrate on non-preemptive hard real-time scheduling algorithms. We compare FIFO, EDLF, SRTF and genetic algorithms for solving this problem. The objective of the scheduling algorithm is to dynamically schedule as many tasks as possible such that each task meets its execution deadline, while minimizing the total delay time of all of the tasks. We present a MicroGA that uses a small populatio... View full abstract»

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  • Improving genetic algorithms for concept learning

    Publication Year: 1994, Page(s):634 - 638 vol.2
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (352 KB)

    In this paper, we argue that the general learning abilities of genetic based techniques for concept learning can be improved in order to deal with numeric and symbolic values, tree-structured values, unknown values and user preference biases. The proposed algorithm, called SIA, uses the covering principle of AQ but with a genetic search that may be called several times. The genetic operators use a... View full abstract»

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  • Automated learning of a detector for the cores of α-helices in protein sequences via genetic programming

    Publication Year: 1994, Page(s):474 - 479 vol.1
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (448 KB)

    The author used J.R. Koza's (1992) genetic programming to evolve programs that classified contiguous regions of proteins as being α-helix cores or not. He snipped positive and negative examples of α-helix core regions out of a set of 90 proteins. These proteins were chosen from the Brookhaven Protein Data Bank to be non-homologous. The fitness of the programs was defined as the correla... View full abstract»

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  • Optimization of fuzzy clustering criteria using genetic algorithms

    Publication Year: 1994, Page(s):589 - 594 vol.2
    Cited by:  Papers (23)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (280 KB)

    This paper introduces a general approach based on genetic algorithms for optimizing a broad class of clustering criteria. The standard approach for optimizing these criteria has been to alternate optimizations between the variables which represent fuzzy memberships of the data to various clusters, and those prototype variables which determine the geometry of the clusters. The approach suggested he... View full abstract»

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  • Evolutionary algorithm for path planning in mobile robot environment

    Publication Year: 1994, Page(s):211 - 216 vol.1
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (424 KB)

    An evolutionary algorithm is discussed for the path planning problem in mobile robot environment, which may contain a number of unknown obstacles. The evolutionary algorithm searches for paths in the entire, continuous free space. It unifies off-line and on-line planning processes and provides high safety measures without requiring complete information about the obstacles sensed View full abstract»

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  • Stack-based genetic programming

    Publication Year: 1994, Page(s):148 - 153 vol.1
    Cited by:  Papers (23)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (392 KB)

    Some recent work in the field of genetic programming (GP) has been concerned with finding optimum representations for evolvable and efficient computer programs. This paper describes a new GP system in which target programs run on a stack-based virtual machine. The system is shown to have certain advantages in terms of efficiency and simplicity of implementation, and for certain problems, its effec... View full abstract»

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  • Scheduling multiple job problems with guided evolutionary simulated annealing approach

    Publication Year: 1994, Page(s):702 - 706 vol.2
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (280 KB)

    This paper reports on an investigation of whether a special type of evolutionary programming named guided evolutionary simulated annealing (GESA) might be used effectively for dealing with scheduling tasks. The GESA approach allows many candidate solutions to be `alive' at the same time. There is local competition and global competition and more and more search resources are guided into promising ... View full abstract»

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  • Dynamic mapping and load balancing with parallel genetic algorithms

    Publication Year: 1994, Page(s):834 - 839 vol.2
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (276 KB)

    The paper presents an approach to dynamic mapping and load balancing of parallel programs in MIMD multicomputers, based on coordinated migration of processes of a parallel program. A program graph is interpreted as a multi-agent system with locally defined goals and actions, operating in some environment. A parallel genetic algorithm (island model) is developed to work out a set of collective deci... View full abstract»

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  • Extended forking genetic algorithm for order representation (o-fGA)

    Publication Year: 1994, Page(s):639 - 644 vol.2
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (348 KB)

    There are two types of GAs with difference of their representation of strings. They are the binary coded GA and the order-based GA. We've already proposed a new type of binary coded GA, called the forking GA (fGA), as a kind of multi-population GA and showed that the searching power of the fGA is superior to the standard GA. The distinguished feature of the fGA is that each population takes a diff... View full abstract»

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  • CAM-Brain: the genetic programming of an artificial brain which grows/evolves at electronic speeds in a cellular automata machine

    Publication Year: 1994, Page(s):337 - 339, 339a-b vol.1
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (368 KB)

    The paper reports on a project which aims to build (i.e. grow/evolve) an artificial brain by the year 2001. This artificial brain should initially contain thousands of interconnected artificial neural network modules, and be capable of controlling approximately 1000 “behaviors” in a “robot kitten”. The name given to this research project is “CAM-Brain”, because ... View full abstract»

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  • A genetic programming application in virtual reality

    Publication Year: 1994, Page(s):480 - 484 vol.1
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (440 KB)

    Genetic programming techniques have been applied to a variety of different problems. The authors discuss the use of these techniques in a virtual environment. The use of genetic programming provides the authors with a quick method of searching shape and sound spaces. The basic design of the system, problems encountered, and future plans are all discussed View full abstract»

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  • Finding improved simulated annealing schedules with genetic programming

    Publication Year: 1994, Page(s):391 - 395 vol.1
    Cited by:  Papers (1)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (416 KB)

    Many combinatorial problems are too difficult to be solved optimally, and hence heuristics are used to obtain “good” solutions in “reasonable” time. A heuristic that has been successfully applied to a variety of problems is simulated annealing. However, the performance of simulated annealing strongly depends on the appropriate choice of a key parameter, the annealing schedu... View full abstract»

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  • The effect of population enrichment in genetic programming

    Publication Year: 1994, Page(s):456 - 461 vol.1
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (332 KB)

    The paper examines the effect of “population enrichment” in genetic programming as a means of efficiently discovering promising directions for solution exploration in a large problem space. With genetic programming it is advantageous to not restrict the size or shape of the solution and enrichment offers an efficient way to present the initial population with interesting options for de... View full abstract»

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  • A comparison of simulated evolution and genetic evolution performance

    Publication Year: 1994, Page(s):374 - 378 vol.1
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (300 KB)

    Simulated evolution (SE) and the genetic algorithm (GA) are closely related methods for finding optimal solutions by directed random search. Both methods start with a population of randomly selected trial solutions and use that information to “evolve” a next generation of trials which, on the average, has improved fitness (i.e., is closer to the optimum). As the population average impr... View full abstract»

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  • Fuzzy evolutionary algorithms and automatic robot trajectory generation

    Publication Year: 1994, Page(s):595 - 600 vol.2
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (460 KB)

    A fuzzy evolutionary algorithm (FEA) is presented by systematically integrating fuzzy expert systems with evolutionary algorithms in this paper. Both computer experiments and applications demonstrate that fuzzy evolutionary algorithms can generally search for optimal solutions faster and more effectively than standard genetic algorithms. As a specific application, a FEA is applied to automatic rob... View full abstract»

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  • Evolving neurocontrollers using evolutionary programming

    Publication Year: 1994, Page(s):217 - 222 vol.1
    Cited by:  Papers (14)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (392 KB)

    Evolutionary programming (EP) is a stochastic optimization technique that can be used to train neural networks. Unlike many training algorithms, EP does not require gradient information, and this facet increases the applicability of the procedure. The current investigation focuses on evolving neurocontrollers for two difficult nonlinear unstable systems. In the first, two separate poles of varying... View full abstract»

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  • S.T.E.P.: the easiest way to optimize a function

    Publication Year: 1994, Page(s):519 - 524 vol.1
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (328 KB)

    Most of the algorithms for global optimization making use of the concept of population exploit very little of the information provided by agents in the population in order to choose the next point to evaluate. We develop a new method called S.T.E.P. (Select The Easiest Point) which determines the next point to evaluate by analysing the usefulness of evaluating the function at a certain position. M... View full abstract»

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  • On the use of a directed acyclic graph to represent a population of computer programs

    Publication Year: 1994, Page(s):154 - 159 vol.1
    Cited by:  Papers (10)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (364 KB)

    This paper demonstrates a technique that reduces the time and space requirements of genetic programming. The population of parse trees is stored as a directed acyclic graph (DAG), rather than as a forest of trees. This saves space by not duplicating structurally identical subtrees. Also, the value computed by each subtree for each fitness case is cached, which saves computation both by not recompu... View full abstract»

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  • A niched Pareto genetic algorithm for multiobjective optimization

    Publication Year: 1994, Page(s):82 - 87 vol.1
    Cited by:  Papers (553)  |  Patents (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (476 KB)

    Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple objectives by incorporating the co... View full abstract»

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  • Evolutionary design of FIR digital filters with power-of-two coefficients

    Publication Year: 1994, Page(s):110 - 114 vol.1
    Cited by:  Papers (3)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (312 KB)

    The paper presents a genetic approach to the design of finite impulse response filters with coefficients constrained to be sums of power-of-two terms. The evolutionary algorithm is explained and compared experimentally with other state-of-the-art design methods. The proposed technique is able to attain good results and can be easily implemented on parallel hardware View full abstract»

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