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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
  • 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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  • Finding maximum flow with random and genetic search

    Publication Year: 1994, Page(s):296 - 299 vol.1
    Cited by:  Papers (2)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (372 KB)

    Solving a maximum flow problem requires finding the greatest balanced flow from a source to a sink in a weighted directional graph. In balanced flow, each node's total input and total output are equal. This paper compares one random and two genetic approaches to finding such solutions. The representation of candidate solutions guarantees balanced flow in all products of mutation and crossover. The... View full abstract»

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  • Two solutions to the general timetable problem using evolutionary methods

    Publication Year: 1994, Page(s):300 - 305 vol.1
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (416 KB)

    The general timetable problem, which involves the placing of events requiring limited resources into timeslots, has been approached in many different ways. This paper describes two approaches to solving the problem using evolutionary algorithms. The methods allow not only the production of feasible timetables but also the evolution of timetables that are `good' with respect to some user-specified ... 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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  • System identification approach to genetic programming

    Publication Year: 1994, Page(s):401 - 406 vol.1
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (496 KB)

    Introduces a new approach to genetic programming (GP), based on a system identification technique, which integrates a GP-based adaptive search of tree structures and a local parameter tuning mechanism employing a statistical search. In Proc. 5th Int. Joint Conf. on Genetic Algorithms (1993), we introduced our adaptive program called STROGANOFF (“STructured Representation On Genetic Algorithm... View full abstract»

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  • Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic algorithm

    Publication Year: 1994, Page(s):306 - 311 vol.1
    Cited by:  Papers (32)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (520 KB)

    Microgenetic algorithms (MGAs) are genetic algorithms that use a very small population size (population size < 10). Recently, interest in MGAs has grown because, for some problems, they are able to find solutions with fewer evaluations than genetic algorithms with larger population sizes. This paper introduces two heuristic-based MGAs which quickly find solutions to constraint satisfaction prob... View full abstract»

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  • A guided evolutionary computation technique as function optimizer

    Publication Year: 1994, Page(s):628 - 633 vol.2
    Cited by:  Papers (5)  |  Patents (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (348 KB)

    In this paper, we present a regionally guided approach to function optimization. The proposed technique is called “Guided Evolutionary Simulated Annealing”. It combines the simulated annealing and simulated evolution in a novel way. The technique has a mechanism that the search will focus on more “promising” areas. The solution is evolved under regional guidance. The charac... View full abstract»

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  • Genetic reinforcement learning for cooperative traffic signal control

    Publication Year: 1994, Page(s):223 - 228 vol.1
    Cited by:  Papers (23)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (416 KB)

    Optimization of a group of traffic signals over an area is a large, multi-agent-type real-time planning problem without a precise reference model being given. To do this planning, each signal should learn not only to acquire its control plans individually through reinforcement learning, but also to cooperate with other signals. These two objectives-distributed learning of agents and cooperation am... 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 (25)
    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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  • A genetic algorithm approach for set covering problems

    Publication Year: 1994, Page(s):569 - 574 vol.2
    Cited by:  Papers (6)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (408 KB)

    We introduce a genetic algorithm approach for set covering problems. Since the set covering problems are constrained optimization problems we utilize a new penalty function to handle the constraints. In addition, we propose a mutation operator which can approach the optima from both sides of feasible/infeasible borders. We experiment with our genetic algorithm to solve several instances of computa... View full abstract»

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  • Parallel evolution of communicating classifier systems

    Publication Year: 1994, Page(s):680 - 685 vol.2
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (332 KB)

    We present an architecture that allows the division of a search space and the parallel solution of the resulting sub-problems. We use multiple genetic algorithms to evolve communicating classifier systems, where each classifier system represents a sub-system of the complete task. Any communication is uninterpreted and emergent to the system, indicating structure and interdependence between the sub... View full abstract»

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  • Genetic drift in sharing methods

    Publication Year: 1994, Page(s):67 - 72 vol.1
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (456 KB)

    Adding a sharing method to a genetic algorithm promotes the formation and maintenance of stable subpopulations. The paper explores the limits of sharing by deriving closed-form expressions for the expected time to disappearance of a subpopulation. The time to disappearance is shown to be an exponential function of population size, with relative subpopulation fitnesses determining the base of the e... View full abstract»

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  • Learning by adapting representations in genetic programming

    Publication Year: 1994, Page(s):407 - 412 vol.1
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (524 KB)

    Machine learning aims towards the acquisition of knowledge, based either on experience from the interaction with the external environment or by analyzing the internal problem-solving traces. Genetic programming (GP) has been effective in learning via interaction, but so far there have not been any significant tests to show that GP can take advantage of its own search traces. This paper demonstrate... 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)
    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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  • Knowledge-based nonuniform crossover

    Publication Year: 1994, Page(s):22 - 27 vol.1
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (288 KB)

    One-point, two-point and k-point crossover can be viewed as special cases of uniform crossover, where genetic material is chosen each locus of either parent with equal probability (G. Syswerda, 1989). The paper generalizes uniform crossover to “non-uniform crossover” using “mask” vectors whose elements are real numbers ∈[0, 1], representing problem-specific knowledge t... View full abstract»

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  • Synthesis of sigma-pi neural networks by the breeder genetic programming

    Publication Year: 1994, Page(s):318 - 323 vol.1
    Cited by:  Papers (4)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (516 KB)

    Genetic programming has been successfully applied to evolve computer programs for solving a variety of interesting problems. The breeder genetic programming (BGP) method has Occam's razor in its fitness measure to evolve minimal size multilayer perceptrons. In this paper, we apply the method to synthesis of sigma-pi neural networks. Unlike perceptron architectures, sigma-pi networks use product un... 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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  • 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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  • Multi-population evolution strategies for structural image analysis

    Publication Year: 1994, Page(s):229 - 234 vol.1
    Cited by:  Papers (2)  |  Patents (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (352 KB)

    To identify objects in aerial images, a special structural approach, based on a blackboard system, is used. The reference objects are described with generic models and a set of real-valued parameters. To adapt these parameters in an automatical way, a closed-loop system is proposed using multi-population evolution strategies with a special form of migration. The result of the parameter optimizatio... 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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  • A genetic approach to the cable harness routing problem

    Publication Year: 1994, Page(s):200 - 205 vol.1
    Cited by:  Papers (4)  |  Patents (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (620 KB)

    This paper describes a system for automatically routing cable harnesses in three-dimensional environments using a pair of genetic algorithms. The cable harness routing problem (CHRP) can be formulated as a graph search problem with a large, convex search space. A genetic approach is used to intelligently and adaptively search for routings which are close to the global optimum. The CHRP is decompos... View full abstract»

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  • An analysis of crossover's effect in genetic algorithms

    Publication Year: 1994, Page(s):613 - 618 vol.2
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (304 KB)

    The crossover operation is characteristic of genetic algorithms (GAs). This paper analyzes the crossover effect in GAs. We start with two bits, that is the minimum chromosome length to crossover. We compare one operator GAs, using only selection, and two operators GAs by selection and crossover with respect to the expected quality and speed of the convergence. First, we analyse the case of two ind... View full abstract»

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  • An evolutionary heuristic for the maximum independent set problem

    Publication Year: 1994, Page(s):531 - 535 vol.2
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (424 KB)

    The results obtained from the application of a genetic algorithm, GENEsYs, to the NP-complete maximum independent set problem are reported. In contrast to many other genetic algorithm-based approaches that use domain-specific knowledge, the approach presented in this paper relies on a graded penalty term component of the fitness function to penalize infeasible solutions. The method is applied to s... View full abstract»

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