2,839 research outputs found

    Effective Fitness Landscapes for Evolutionary Systems

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    In evolution theory the concept of a fitness landscape has played an important role, evolution itself being portrayed as a hill-climbing process on a rugged landscape. In this article it is shown that in general, in the presence of other genetic operators such as mutation and recombination, hill-climbing is the exception rather than the rule. This descrepency can be traced to the different ways that the concept of fitness appears --- as a measure of the number of fit offspring, or as a measure of the probability to reach reproductive age. Effective fitness models the former not the latter and gives an intuitive way to understand population dynamics as flows on an effective fitness landscape when genetic operators other than selection play an important role. The efficacy of the concept is shown using several simple analytic examples and also some more complicated cases illustrated by simulations.Comment: 11 pages, 8 postscript figure

    How Creative Should Creators be to Optimize the Evolution of Ideas? A Computer Model

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    There are both benefits and drawbacks to creativity. In a social group it is not necessary for all members to be creative to benefit from creativity; some merely imitate or enjoy the fruits of others' creative efforts. What proportion should be creative? This paper outlines investigations of this question carried out using a computer model of cultural evolution referred to as EVOC (for EVOlution of Culture). EVOC is composed of neural network based agents that evolve fitter ideas for actions by (1) inventing new ideas through modification of existing ones, and (2) imitating neighbors' ideas. The ideal proportion with respect to fitness of ideas is found to depend on the level of creativity of the creative agents. For all levels or creativity, the diversity of ideas in a population is positively correlated with the ratio of creative agents

    A graphical user interface for evolutionary algorithms

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    The purpose of Generic Evolutionary Algorithms Programming Library (GEA1) system is to provide researchers with an easy-to-use, widely applicable and extendable programming library which solves real-world optimization problems by means of evolutionary algorithms. It contains algorithms for various evolutionary methods, implemented genetic operators for the most common representation forms for individuals, various selection methods, and examples on how to use and expand the library. All these functions assure that GEA can be effectively applied on many problems. GraphGEA is a graphical user interface to GEA written with the GTK API. The numerous parameters of the evolutionary algorithm can be set in appropriate dialog boxes. The program also checks the correctness of the parameters and saving/restoring of parameter sets is also possible. The selected evolutionary algorithm can be executed interactively on the specified optimization problem through the graphical user interface of GraphGEA, and the results and behavior of the EA can be observed on several selected graphs and drawings. While the main purpose of GEA is solving optimization problems, that of GraphGEA is education and analysis. It can be of great help for students understanding the characteristics of evolutionary algorithms and researchers of the area can use it to analyze an EA's behavior on particular problems

    Costs and benefits of tuning parameters of evolutionary algorithms

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    Abstract. We present an empirical study on the impact of different design choices on the performance of an evolutionary algorithm (EA). Four EA components are considered—parent selection, survivor selection, recombination and mutation—and for each component we study the impact of choosing the right operator and of tuning its free parameter(s). We tune 120 different combinations of EA operators to 4 different classes of fitness landscapes and measure the cost of tuning. We find that components differ greatly in importance. Typically the choice of operator for parent selection has the greatest impact, and mutation needs the most tuning. Regarding individual EAs however, the impact of design choices for one component depends on the choices for other components, as well as on the available amount of resources for tuning.

    CSM429: Abstract Geometric Crossover for the Permutation Representation

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    Abstract crossover and abstract mutation are representation-independent operators that are well-defined once a notion of distance over the solution space is defined. They were obtained as generalization of genetic operators for binary strings and real vectors. In this paper we explore how the abstract geometric framework applies to the permutation representation. This representation is challenging for various reasons: because of the inherent difference between permutations and the representations that inspired the abstraction; because the whole notion of geometry over permutation spaces radically departs from traditional geometries and it is almost unexplored mathematical territory; because the many notions of distance available and their subtle interconnections make it hard to see the right distance to use, if any; because the various available interpretations of permutations make ambiguous what a permutation represents, hence, how to treat it; because of the existence of various permutation-like representations that are incorrectly confused with permutations; and finally because of the existence of many mutation and recombination operators and their many variations for the same representation. This article shows that the application of our geometric framework naturally clarifies and unifies an important domain,the permutation representation and the related operators, in which there was little or no hope to find order. In addition the abstract geometric framework is used to improve the design of crossover operators for well-known problems naturally connected with the permutation representation

    A Parallel General Purpose Multi-Objective Optimization Framework, with Application to Beam Dynamics

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    Particle accelerators are invaluable tools for research in the basic and applied sciences, in fields such as materials science, chemistry, the biosciences, particle physics, nuclear physics and medicine. The design, commissioning, and operation of accelerator facilities is a non-trivial task, due to the large number of control parameters and the complex interplay of several conflicting design goals. We propose to tackle this problem by means of multi-objective optimization algorithms which also facilitate a parallel deployment. In order to compute solutions in a meaningful time frame a fast and scalable software framework is required. In this paper, we present the implementation of such a general-purpose framework for simulation-based multi-objective optimization methods that allows the automatic investigation of optimal sets of machine parameters. The implementation is based on a master/slave paradigm, employing several masters that govern a set of slaves executing simulations and performing optimization tasks. Using evolutionary algorithms as the optimizer and OPAL as the forward solver, validation experiments and results of multi-objective optimization problems in the domain of beam dynamics are presented. The high charge beam line at the Argonne Wakefield Accelerator Facility was used as the beam dynamics model. The 3D beam size, transverse momentum, and energy spread were optimized

    On the Benefits of Inoculation, an Example in Train Scheduling

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    The local reconstruction of a railway schedule following a small perturbation of the traffic, seeking minimization of the total accumulated delay, is a very difficult and tightly constrained combinatorial problem. Notoriously enough, the railway company's public image degrades proportionally to the amount of daily delays, and the same goes for its profit! This paper describes an inoculation procedure which greatly enhances an evolutionary algorithm for train re-scheduling. The procedure consists in building the initial population around a pre-computed solution based on problem-related information available beforehand. The optimization is performed by adapting times of departure and arrival, as well as allocation of tracks, for each train at each station. This is achieved by a permutation-based evolutionary algorithm that relies on a semi-greedy heuristic scheduler to gradually reconstruct the schedule by inserting trains one after another. Experimental results are presented on various instances of a large real-world case involving around 500 trains and more than 1 million constraints. In terms of competition with commercial math ematical programming tool ILOG CPLEX, it appears that within a large class of instances, excluding trivial instances as well as too difficult ones, and with very few exceptions, a clever initialization turns an encouraging failure into a clear-cut success auguring of substantial financial savings
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