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Evolutionary Computation, IEEE Transactions on

Issue 2 • Date April 2009

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Displaying Results 1 - 24 of 24
  • Table of contents

    Page(s): C1
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    Freely Available from IEEE
  • IEEE Transactions on Evolutionary Computation publication information

    Page(s): C2
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    Freely Available from IEEE
  • Lower and Upper Bounds for Linkage Discovery

    Page(s): 201 - 216
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (591 KB) |  | HTML iconHTML  

    For a real-valued function f defined on {0,1}n , the linkage graph of f is a hypergraph that represents the interactions among the input variables with respect to f . In this paper, lower and upper bounds for the number of function evaluations required to discover the linkage graph are rigorously analyzed in the black box scenario. First, a lower bound for discovering linkage graph is presented. To the best of our knowledge, this is the first result on the lower bound for linkage discovery. The investigation on the lower bound is based on Yao's minimax principle. For the upper bounds, a simple randomized algorithm for linkage discovery is analyzed. Based on the Kruskal-Katona theorem, we present an upper bound for discovering the linkage graph. As a corollary, we rigorously prove that O(n 2logn) function evaluations are enough for bounded functions when the number of hyperedges is O(n), which was suggested but not proven in previous works. To see the typical behavior of the algorithm for linkage discovery, three random models of fitness functions are considered. Using probabilistic methods, we prove that the number of function evaluations on the random models is generally smaller than the bound for the arbitrary case. Finally, from the relation between the linkage graph and the Walsh coefficients, it is shown that, for bounded functions, the proposed bounds are eventually the bounds for finding the Walsh coefficients. View full abstract»

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  • Associative Classification With Artificial Immune System

    Page(s): 217 - 228
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1020 KB) |  | HTML iconHTML  

    Associative classification (AC), which is based on association rules, has shown great promise over many other classification techniques. To implement AC effectively, we need to tackle the problems on the very large search space of candidate rules during the rule discovery process and incorporate the discovered association rules into the classification process. This paper proposes a new approach that we call artificial immune system-associative classification (AIS-AC), which is based on AIS, for mining association rules effectively for classification. Instead of massively searching for all possible association rules, AIS-AC will only find a subset of association rules that are suitable for effective AC in an evolutionary manner. In this paper, we also evaluate the performance of the proposed AIS-AC approach for AC based on large datasets. The performance results have shown that the proposed approach is efficient in dealing with the problem on the complexity of the rule search space, and at the same time, good classification accuracy has been achieved. This is especially important for mining association rules from large datasets in which the search space of rules is huge. View full abstract»

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  • Multiobjective Groundwater Management Using Evolutionary Algorithms

    Page(s): 229 - 242
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1900 KB) |  | HTML iconHTML  

    Sustainable management of groundwater resources is of crucial importance for regions where freshwater supply is naturally limited. Long-term planning of groundwater usage requires computer-based decision support tools: on the one hand, they must be able to predict the complex system dynamics with sufficient accuracy, on the other, they must allow exploring management scenarios with respect to different criteria such as sustainability, cost, etc. In this paper, we present a multiobjective evolutionary algorithm for groundwater management that optimizes the placement and the operation of pumping facilities over time, while considering multiple neighboring regions which are economically independent. The algorithm helps in investigating the cost tradeoffs between the different regions by providing an approximation of the Pareto-optimal set, and its capabilities are demonstrated on a three-region problem. The application of the proposed methodology can also serve as a benchmark problem as shown in this paper. The corresponding implementation is freely available as a precompiled module at http://www.tik.ee.ethz.ch/pisa. View full abstract»

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  • Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces

    Page(s): 243 - 259
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1879 KB) |  | HTML iconHTML  

    Many different algorithms have been developed in the last few decades for solving complex real-world search and optimization problems. The main focus in this research has been on the development of a single universal genetic operator for population evolution that is always efficient for a diverse set of optimization problems. In this paper, we argue that significant advances to the field of evolutionary computation can be made if we embrace a concept of self-adaptive multimethod optimization in which multiple different search algorithms are run concurrently, and learn from each other through information exchange using a common population of points. We present an evolutionary algorithm, entitled A Multialgorithm Genetically Adaptive Method for Single Objective Optimization (AMALGAM-SO), that implements this concept of self adaptive multimethod search. This method simultaneously merges the strengths of the covariance matrix adaptation (CMA) evolution strategy, genetic algorithm (GA), and particle swarm optimizer (PSO) for population evolution and implements a self-adaptive learning strategy to automatically tune the number of offspring these three individual algorithms are allowed to contribute during each generation. Benchmark results in 10, 30, and 50 dimensions using synthetic functions from the special session on real-parameter optimization of CEC 2005 show that AMALGAM-SO obtains similar efficiencies as existing algorithms on relatively simple unimodal problems, but is superior for more complex higher dimensional multimodal optimization problems. The new search method scales well with increasing number of dimensions, converges in the close proximity of the global minimum for functions with noise induced multimodality, and is designed to take full advantage of the power of distributed computer networks. View full abstract»

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  • Fuzzy-UCS: A Michigan-Style Learning Fuzzy-Classifier System for Supervised Learning

    Page(s): 260 - 283
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1932 KB) |  | HTML iconHTML  

    This paper presents Fuzzy-UCS, a Michigan-style learning fuzzy-classifier system specifically designed for supervised learning tasks. Fuzzy-UCS is inspired by UCS, an on-line accuracy-based learning classifier system. Fuzzy-UCS introduces a linguistic representation of the rules with the aim of evolving more readable rule sets, while maintaining similar performance and generalization capabilities to those presented by UCS. The behavior of Fuzzy-UCS is analyzed in detail from several perspectives. The granularity of the linguistic fuzzy representation to define complex decision boundaries is illustrated graphically, and the test performance obtained with different inference schemes is studied. Fuzzy-UCS is also compared with a large set of other fuzzy and nonfuzzy learners, demonstrating the competitiveness of its on-line architecture in terms of performance and interpretability. Finally, the paper shows the advantages obtained when Fuzzy-UCS is applied to learn fuzzy models from large volumes of data. View full abstract»

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  • Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II

    Page(s): 284 - 302
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3518 KB) |  | HTML iconHTML  

    Partly due to lack of test problems, the impact of the Pareto set (PS) shapes on the performance of evolutionary algorithms has not yet attracted much attention. This paper introduces a general class of continuous multiobjective optimization test instances with arbitrary prescribed PS shapes, which could be used for studying the ability of multiobjective evolutionary algorithms for dealing with complicated PS shapes. It also proposes a new version of MOEA/D based on differential evolution (DE), i.e., MOEA/D-DE, and compares the proposed algorithm with NSGA-II with the same reproduction operators on the test instances introduced in this paper. The experimental results indicate that MOEA/D could significantly outperform NSGA-II on these test instances. It suggests that decomposition based multiobjective evolutionary algorithms are very promising in dealing with complicated PS shapes. View full abstract»

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  • Evolution and Incremental Learning in the Iterated Prisoner's Dilemma

    Page(s): 303 - 320
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2470 KB) |  | HTML iconHTML  

    This paper examines the comparative performance and adaptability of evolutionary, learning, and memetic strategies to different environment settings in the iterated prisoner's dilemma (IPD). A memetic adaptation framework is developed for IPD strategies to exploit the complementary features of evolution and learning. In the paradigm, learning serves as a form of directed search to guide evolving strategies to attain eventual convergence towards good strategy traits, while evolution helps to minimize disparity in performance among learning strategies. Furthermore, a double-loop incremental learning scheme (ILS) that incorporates a classification component, probabilistic update of strategies and a feedback learning mechanism is proposed and incorporated into the evolutionary process. A series of simulation results verify that the two techniques, when employed together, are able to complement each other's strengths and compensate for each other's weaknesses, leading to the formation of strategies that will adapt and thrive well in complex, dynamic environments. View full abstract»

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  • Accelerating Self-Modeling in Cooperative Robot Teams

    Page(s): 321 - 332
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1492 KB) |  | HTML iconHTML  

    One of the major obstacles to achieving robots capable of operating in real-world environments is enabling them to cope with a continuous stream of unanticipated situations. In previous work, it was demonstrated that a robot can autonomously generate self-models, and use those self-models to diagnose unanticipated morphological change such as damage. In this paper, it is shown that multiple physical quadrupedal robots with similar morphologies can share self-models in order to accelerate modeling. Further, it is demonstrated that quadrupedal robots which maintain separate self-modeling algorithms but swap self-models perform better than quadrupedal robots that rely on a shared self-modeling algorithm. This finding points the way toward more robust robot teams: a robot can diagnose and recover from unanticipated situations faster by drawing on the previous experiences of the other robots. View full abstract»

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  • Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming

    Page(s): 333 - 349
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1123 KB) |  | HTML iconHTML  

    This paper presents a novel approach to generate data-driven regression models that not only give reliable prediction of the observed data but also have smoother response surfaces and extra generalization capabilities with respect to extrapolation. These models are obtained as solutions of a genetic programming (GP) process, where selection is guided by a tradeoff between two competing objectives - numerical accuracy and the order of nonlinearity. The latter is a novel complexity measure that adopts the notion of the minimal degree of the best-fit polynomial, approximating an analytical function with a certain precision. Using nine regression problems, this paper presents and illustrates two different strategies for the use of the order of nonlinearity in symbolic regression via GP. The combination of optimization of the order of nonlinearity together with the numerical accuracy strongly outperforms ldquoconventionalrdquo optimization of a size-related expressional complexity and the accuracy with respect to extrapolative capabilities of solutions on all nine test problems. In addition to exploiting the new complexity measure, this paper also introduces a novel heuristic of alternating several optimization objectives in a 2-D optimization framework. Alternating the objectives at each generation in such a way allows us to exploit the effectiveness of 2-D optimization when more than two objectives are of interest (in this paper, these are accuracy, expressional complexity, and the order of nonlinearity). Results of the experiments on all test problems suggest that alternating the order of nonlinearity of GP individuals with their structural complexity produces solutions that are both compact and have smoother response surfaces, and, hence, contributes to better interpretability and understanding. View full abstract»

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  • Boundary Search for Constrained Numerical Optimization Problems With an Algorithm Inspired by the Ant Colony Metaphor

    Page(s): 350 - 368
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1292 KB) |  | HTML iconHTML  

    This paper presents a novel boundary approach that is included as a constraint-handling technique in an algorithm inspired by the ant colony metaphor. The necessity of approaching the boundary between the feasible and infeasible search space for many constrained optimization problems is a paramount challenge for every constraint-handling technique. Our proposed technique precisely focuses the search on the boundary region and can be either used alone or in combination with other constraint-handling techniques depending on the type and number of problem constraints. For validation purposes, an algorithm inspired by the ant colony metaphor is adopted as our search engine that works following one of the principles of the ant colony approach, i.e., a population of agents iteratively, cooperatively, and independently search for a solution. Each ant in the distributed algorithm applies a simple mutation-like operator, which explores the neighborhood region of a particular point in the search space (individual search level). The operator is designed for exploring the boundary between the feasible and infeasible search space. In addition, each ant obtains global information from the colony in order to exploit the most promising regions of the search space (cooperation level). We compare our proposed approach with respect to a well-known constraint-handling technique that is representative of the state-of-the-art in the area, using a set of standard test functions. View full abstract»

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  • Limitations of Existing Mutation Rate Heuristics and How a Rank GA Overcomes Them

    Page(s): 369 - 397
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3807 KB) |  | HTML iconHTML  

    Using a set of different search metrics and a set of model landscapes we theoretically and empirically study how ldquooptimalrdquo mutation rates for the simple genetic algorithm (SGA) depend not only on the fitness landscape, but also on population size and population state. We discuss the limitations of current mutation rate heuristics, showing that any fixed mutation rate can be expected to be suboptimal in terms of balancing exploration and exploitation. We then develop a mutation rate heuristic that offers a better balance by assigning different mutation rates to different subpopulations. When the mutation rate is assigned through a ranking of the population, according to fitness for example, we call the resulting algorithm a Rank GA. We show how this Rank GA overcomes the limitations of other heuristics on a set of model problems showing under what circumstances it might be expected to outperform a SGA with any choice of mutation rate. View full abstract»

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  • Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization

    Page(s): 398 - 417
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1653 KB) |  | HTML iconHTML  

    Differential evolution (DE) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been widely applied in many scientific and engineering fields. However, the success of DE in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies and their associated control parameter values. Employing a trial-and-error scheme to search for the most suitable strategy and its associated parameter settings requires high computational costs. Moreover, at different stages of evolution, different strategies coupled with different parameter settings may be required in order to achieve the best performance. In this paper, we propose a self-adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, a more suitable generation strategy along with its parameter settings can be determined adaptively to match different phases of the search process/evolution. The performance of the SaDE algorithm is extensively evaluated (using codes available from P. N. Suganthan) on a suite of 26 bound-constrained numerical optimization problems and compares favorably with the conventional DE and several state-of-the-art parameter adaptive DE variants. View full abstract»

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  • Interactive Evolution of Particle Systems for Computer Graphics and Animation

    Page(s): 418 - 432
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2141 KB) |  | HTML iconHTML  

    Interactive Evolutionary Computation (IEC) creates the intriguing possibility that a large variety of useful content can be produced quickly and easily for practical computer graphics and gaming applications. To show that IEC can produce such content, this paper applies IEC to particle system effects, which are the de facto method in computer graphics for generating fire, smoke, explosions, electricity, water, and many other special effects. While particle systems are capable of producing a broad array of effects, they require substantial mathematical and programming knowledge to produce. Therefore, efficient particle system generation tools are required for content developers to produce special effects in a timely manner. This paper details the design, representation, and animation of particle systems via two IEC tools called NEAT Particles and NEAT Projectiles. Both tools evolve artificial neural networks (ANN) with the NeuroEvolution of Augmenting Topologies (NEAT) method to control the behavior of particles. NEAT Particles evolves general-purpose particle effects, whereas NEAT Projectiles specializes in evolving particle weapon effects for video games. The primary advantage of this NEAT-based IEC approach is to decouple the creation of new effects from mathematics and programming, enabling content developers without programming knowledge to produce complex effects. Furthermore, it allows content designers to produce a broader range of effects than typical development tools. Finally, it acts as a concept generator, allowing content creators to interactively and efficiently explore the space of possible effects. Both NEAT Particles and NEAT Projectiles demonstrate how IEC can evolve useful content for graphical media and games, and are together a step toward the larger goal of automated content generation. View full abstract»

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  • An Evolutionary Squeaky Wheel Optimization Approach to Personnel Scheduling

    Page(s): 433 - 443
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (567 KB) |  | HTML iconHTML  

    The quest for robust heuristics that are able to solve more than one problem is ongoing. In this paper, we present, discuss and analyze a technique called Evolutionary Squeaky Wheel Optimization and apply it to two different personnel scheduling problems. Evolutionary Squeaky Wheel Optimization improves the original squeaky wheel optimization's effectiveness and execution speed by incorporating two additional steps (selection and mutation) for added evolution. In the evolutionary squeaky wheel optimization, a cycle of analysis-selection-mutation-prioritization-construction continues until stopping conditions are reached. The aim of the analysis step is to identify below average solution components by calculating a fitness value for all components. The selection step then chooses amongst these underperformers and discards some probabilistically based on fitness. The mutation step further discards a few components at random. Solutions can become incomplete and thus repairs may be required. The repair is carried out by using the prioritization step to first produce priorities that determine an order by which the following construction step then schedules the remaining components. Therefore, improvements in the evolutionary squeaky wheel optimization is achieved by selective solution disruption mixed with iterative improvement and constructive repair. Strong experimental results are reported on two different domains of personnel scheduling: bus and rail driver scheduling and hospital nurse scheduling. View full abstract»

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  • Evolutionary Algorithms for Minimax Problems in Robust Design

    Page(s): 444 - 453
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (688 KB) |  | HTML iconHTML  

    Many robust design problems can be described by minimax optimization problems. Classical techniques for solving these problems have typically been limited to a discrete form of the problem. More recently, evolutionary algorithms, particularly coevolutionary optimization techniques, have been applied to minimax problems. A new method of solving minimax optimization problems using evolutionary algorithms is proposed. The performance of this algorithm is shown to compare favorably with the existing methods on test problems. The performance of the algorithm is demonstrated on a robust pole placement problem and a ship engineering plant design problem. View full abstract»

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  • A Genetic Algorithm That Adaptively Mutates and Never Revisits

    Page(s): 454 - 472
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1726 KB) |  | HTML iconHTML  

    A novel genetic algorithm is reported that is non-revisiting: It remembers every position that it has searched before. An archive is used to store all the solutions that have been explored before. Different from other memory schemes in the literature, a novel binary space partitioning tree archive design is advocated. Not only is the design an efficient method to check for revisits, if any, it in itself constitutes a novel adaptive mutation operator that has no parameter. To demonstrate the power of the method, the algorithm is evaluated using 19 famous benchmark functions. The results are as follows. (1) Though it only uses finite resolution grids, when compared with a canonical genetic algorithm, a generic real-coded genetic algorithm, a canonical genetic algorithm with simple diversity mechanism, and three particle swarm optimization algorithms, it shows a significant improvement. (2) The new algorithm also shows superior performance compared to covariance matrix adaptation evolution strategy (CMA-ES), a state-of-the-art method for adaptive mutation. (3) It can work with problems that have large search spaces with dimensions as high as 40. (4) The corresponding CPU overhead of the binary space partitioning tree design is insignificant for applications with expensive or time-consuming fitness evaluations, and for such applications, the memory usage due to the archive is acceptable. (5) Though the adaptive mutation is parameter-less, it shows and maintains a stable good performance. However, for other algorithms we compare, the performance is highly dependent on suitable parameter settings. View full abstract»

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  • IEEE Congress on Evolutionary Computation

    Page(s): 473
    Save to Project icon | Request Permissions | PDF file iconPDF (645 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Autonomous Mental Development call for papers

    Page(s): 474
    Save to Project icon | Request Permissions | PDF file iconPDF (525 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Computational Intelligence and AI in Games

    Page(s): 475
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    Freely Available from IEEE
  • Scitopia.org [advertisement]

    Page(s): 476
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    Freely Available from IEEE
  • IEEE Computational Intelligence Society Information

    Page(s): C3
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    Freely Available from IEEE
  • IEEE Transactions on Evolutionary Computation Information for authors

    Page(s): C4
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    Freely Available from IEEE

Aims & Scope

IEEE Transactions on Evolutionary Computation publishes archival quality original papers in evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined. Purely theoretical papers are considered as are application papers that provide general insights into these areas of computation.
 

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Garrison W. Greenwood, Ph.D. P.E
Portland State University