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
  • Solving constraint satisfaction problems using genetic algorithms

    Publication Year: 1994, Page(s):542 - 547 vol.2
    Cited by:  Papers (26)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (460 KB)

    This article discusses the applicability of genetic algorithms (GAs) to solve constraint satisfaction problems (CSPs). We discuss the requirements and possibilities of defining so-called heuristic GAs (HGAs), which can be expected to be effective and efficient methods to solve CSPs since they adopt heuristics used in classical CSP solving search techniques. We present and analyse experimental resu... View full abstract»

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  • Genetic algorithm guided clustering

    Publication Year: 1994, Page(s):34 - 39 vol.1
    Cited by:  Papers (23)  |  Patents (20)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (320 KB)

    Genetic algorithms provide an approach to optimization. Unsupervised clustering algorithms attempt to optimize the placement of like objects into homogeneous classes or clusters. We describe an approach to using genetic algorithms to optimize the clusters created during unsupervised clustering. Hard partitions of the feature space are the members of the population. They evolve into better partitio... View full abstract»

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  • Balancing exploration with exploitation-solving mazes with real numbered search spaces

    Publication Year: 1994, Page(s):485 - 489 vol.1
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (356 KB)

    A hybrid architecture, called EXP1, automatically balances exploration and exploitation to solve mazes with large real numbered search spaces. It employs a genetic algorithm (GA) to search and optimise each movement. The GA fitness function is supplied by a radial basis function (RBF) neural network which acts as an adaptive heuristic critic (AHC). Over successive trials it learns the V-function, ... View full abstract»

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  • Using a genetic algorithm to optimize problems with feasibility constraints

    Publication Year: 1994, Page(s):548 - 553 vol.2
    Cited by:  Papers (27)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (428 KB)

    G.E. Liepins et al. (1990) have shown that genetic algorithm optimization of certain combinatorial optimization problems can be more effective when the genetic algorithm evaluates “repaired” versions of the chromosomes. In this sense “repairing” a chromosome means to take an illegal chromosome and force it to be legal through some repair function, Liepens does not however, ... 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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  • Evolution strategies applied to perturbed objective functions

    Publication Year: 1994, Page(s):40 - 45 vol.1
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (424 KB)

    We investigate the behavior of evolution strategies on noisy objective functions. We show for the simple sphere model that convergence velocity is not reduced as long as the noise level is small compared to the function value. If the noise level reaches a certain threshold, a size of the parent population greater than 1 improves the convergence precision significantly. Convergence reliability is t... View full abstract»

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  • An empirical comparison of two evolutionary methods for satisfiability problems

    Publication Year: 1994, Page(s):450 - 455 vol.1
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (484 KB)

    The paper compares two evolutionary methods for model finding in the satisfiability problem (SAT): genetic algorithms (GAs) and the mask method (MASK). The main characteristics of these two methods are that both of them are population-based, and use binary representation. Great care is taken to make sure that the same SAT instances and the same criteria are used in the comparison. Results indicate... View full abstract»

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  • A genetic adaptive algorithm for data equalization

    Publication Year: 1994, Page(s):665 - 669 vol.2
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (280 KB)

    A technique for adjusting the coefficients of adaptive IIR equalizing filters using a genetic algorithm is presented. This technique is shown to be able to significantly reduce the intersymbol interference and noise created by imperfect communications channel transmission characteristics View full abstract»

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  • Improving classification performance in the bumptree network by optimising topology with a genetic algorithm

    Publication Year: 1994, Page(s):490 - 495 vol.1
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (448 KB)

    This paper presents a successful synthesis of evolutionary and connectionist methods, based on the genetic optimisation of a recently introduced neural network model, the bumptree network. We show that the bumptree network is inherently more suited to optimisation by a genetic algorithm (GA) than other neural network models such as the multi-layer perceptron (MLP). We describe a hierarchical genet... View full abstract»

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  • Penalty functions and the knapsack problem

    Publication Year: 1994, Page(s):554 - 558 vol.2
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (308 KB)

    This paper reports on a study of the effectiveness of penalty functions used with a standard genetic algorithm to solve a problem with constraints. Twelve different penalty functions were created and tested using a genetic algorithm to solve the zero-one knapsack problem. In addition to a comparison of the penalty functions, the relationship between the size of the solution space and the size of t... View full abstract»

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  • Genetic algorithm and simulated annealing for optimal robot arm PID control

    Publication Year: 1994, Page(s):707 - 713 vol.2
    Cited by:  Papers (28)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (420 KB)

    This paper describes the use of genetic algorithm (GA) and simulated annealing (SA) for optimizing the parameters of PID controllers for a 6-DOF robot arm. A GA and a SA are designed to optimal-tune the parameters of the PID controller of each joint for a single step response and for the tracking of other specified trajectories. The GA and the SA are required to optimize evaluation functions relat... View full abstract»

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  • Evolutionary design of application tailored neural networks

    Publication Year: 1994, Page(s):284 - 289 vol.1
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (460 KB)

    An evolutionary algorithm for designing single hidden-layer feedforward neural networks is proposed. The algorithm constructs a problem-tailored neural network by incremental introduction of new hidden units. Each new hidden unit is added to the network by linear partitioning of the hidden-layer representation through a genetic search. A two-stage algorithm speed-up is achieved through: (1) a dist... View full abstract»

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  • A parallel genetic algorithm on the CM-2 for multi-modal optimization

    Publication Year: 1994, Page(s):818 - 822 vol.2
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (356 KB)

    A genetic algorithm is an optimization method well suited to be implemented on a SIMD machine; it deals with a population of individuals (Multiple Data) that evolve in parallel and undergo the same operations (Single Instruction). This paper presents a genetic algorithm with a dynamic division mechanism conceived on the Connection Machine-2 to treat multimodal optimization problems, i.e. search sp... View full abstract»

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  • Fuzzy clustering with genetic search

    Publication Year: 1994, Page(s):46 - 50 vol.1
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (400 KB)

    Pattern classification task consists of clustering the training samples into known classes and using these clusters to classify new samples. Clustering is done by finding an appropriate set of ellipsoids for enclosing each of the classes. To obtain fuzzy clustering, membership values are assigned to samples against ellipsoids of all classes and these values are defuzzified for final classification... View full abstract»

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  • Evolution of CIM system with genetic algorithm

    Publication Year: 1994, Page(s):746 - 749 vol.2
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (296 KB)

    The paper presents a basic concept of evolution of computer integrated manufacturing (CIM) with genetic algorithm (GA). The paper presents a multi-agent model of the CIM system. Each agent solves its own manufacturing problem independently using the GA and has a function of strategic search through interactions with the environment. The new model can produce better solutions. This method is applie... 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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  • Genetic algorithms for network division problem

    Publication Year: 1994, Page(s):422 - 427 vol.1
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (424 KB)

    The network division problem (NDP), whereby a network (graph) of hosts (nodes) needs to be segmented into a given number of partitions with a penalty incurred for communication between partitions, is a fundamental problem in computer networks and distributed systems. The problem is a variation of a k-way graph partitioning problem and is thus NP-complete. The algorithmic methods for finding an opt... View full abstract»

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  • A delayed-action classifier system for learning in temporal environments

    Publication Year: 1994, Page(s):670 - 673 vol.2
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (292 KB)

    This paper describes a modified version of the traditional classifier system called the Delayed Action Classifier System (DACS) which has been conceived for learning in environments that exhibit a rich temporal structure. DACS operates by delaying the action of appropriately tagged classifiers (called `delayed-action classifiers') by a number of execution cycles which is encoded on the action part... View full abstract»

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  • Improving game-tree search with evolutionary neural networks

    Publication Year: 1994, Page(s):496 - 501 vol.1
    Cited by:  Papers (5)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (532 KB)

    Neural networks were evolved to constrain minimax search in the game of Othello. At each level of the search tree, such focus networks decide which moves are to be explored. Based on the evolved knowledge of the minimax algorithm's advantages and limitations the networks hide problem nodes from minimax. Focus networks were encoded in marker-based chromosomes and evolved against a full-width minima... View full abstract»

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  • Artificial selection in a system of self-replicating strings

    Publication Year: 1994, Page(s):651 - 655 vol.2
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (256 KB)

    Increasingly, artificial life (AL) models are introduced to study various aspects of the formal foundation of the phenomenon of life. Metabolism and the ability to self-replicate have long been considered as the most prominent of these aspects. It is natural to ask, how AL-systems might be used in application problems of today. Since AL-systems are based on competition between entities, which brin... View full abstract»

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  • The genetic algorithm for 0-1 programming with linear constraints

    Publication Year: 1994, Page(s):559 - 564 vol.2
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (284 KB)

    This paper suggests a new method for solving 0-1 programming problem with linear constraints by genetic algorithm. The existing way of handling such problem allows constraint violation at the expense of some penalty and meets some difficulties of choosing penalty parameter which affects directly numerical result and possible premature convergence. In order to improve this situation, a genetic algo... View full abstract»

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  • Collision avoidance planning of a robot manipulator by using genetic algorithm. A consideration for the problem in which moving obstacles and/or several robots are included in the workspace

    Publication Year: 1994, Page(s):714 - 719 vol.2
    Cited by:  Papers (12)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (352 KB)

    There have been proposed various approaches for solving the collision avoidance problem of a robot manipulator. However, unfortunately, almost all of the research in this area has so far only dealt with the collision avoidance problem in which moving obstacles are not included in the workspace of a robot manipulator. In this paper, it is shown that path planning and the collision avoidance plannin... View full abstract»

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  • NN's and GA's: evolving co-operative behaviour in adaptive learning agents

    Publication Year: 1994, Page(s):290 - 295 vol.1
    Cited by:  Papers (1)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (444 KB)

    Without a comprehensive training set, it is difficult to train neural networks (NN) to solve a complex learning task. Usually, the more complex the problem or task the NNs have to learn, the less likely it is that there is a realistic training set that could be used for (supervised) training. One way to overcome this limitation is to implement an evolutionary approach to train NNs. We report the r... View full abstract»

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