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Evolutionary Computation, IEEE Transactions on

Issue 1 • Date Feb. 2010

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  • Table of contents

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

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  • Analysis of Computational Time of Simple Estimation of Distribution Algorithms

    Page(s): 1 - 22
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (329 KB) |  | HTML iconHTML  

    Estimation of distribution algorithms (EDAs) are widely used in stochastic optimization. Impressive experimental results have been reported in the literature. However, little work has been done on analyzing the computation time of EDAs in relation to the problem size. It is still unclear how well EDAs (with a finite population size larger than two) will scale up when the dimension of the optimization problem (problem size) goes up. This paper studies the computational time complexity of a simple EDA, i.e., the univariate marginal distribution algorithm (UMDA), in order to gain more insight into EDAs complexity. First, we discuss how to measure the computational time complexity of EDAs. A classification of problem hardness based on our discussions is then given. Second, we prove a theorem related to problem hardness and the probability conditions of EDAs. Third, we propose a novel approach to analyzing the computational time complexity of UMDA using discrete dynamic systems and Chernoff bounds. Following this approach, we are able to derive a number of results on the first hitting time of UMDA on a well-known unimodal pseudo-boolean function, i.e., the LeadingOnes problem, and another problem derived from LeadingOnes, named BVLeadingOnes. Although both problems are unimodal, our analysis shows that LeadingOnes is easy for the UMDA, while BVLeadingOnes is hard for the UMDA. Finally, in order to address the key issue of what problem characteristics make a problem hard for UMDA, we discuss in depth the idea of ??margins?? (or relaxation). We prove theoretically that the UMDA with margins can solve the BVLeadingOnes problem efficiently. View full abstract»

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  • Interaction of Culture-Based Learning and Cooperative Co-Evolution and its Application to Automatic Behavior-Based System Design

    Page(s): 23 - 57
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1388 KB) |  | HTML iconHTML  

    Designing an intelligent situated agent is a difficult task because the designer must see the problem from the agent's viewpoint, considering all its sensors, actuators, and computation systems. In this paper, we introduce a bio-inspired hybridization of reinforcement learning, cooperative co-evolution, and a cultural-inspired memetic algorithm for the automatic development of behavior-based agents. Reinforcement learning is responsible for the individual-level adaptation. Cooperative co-evolution performs at the population level and provides basic decision-making modules for the reinforcement-learning procedure. The culture-based memetic algorithm, which is a new computational interpretation of the meme metaphor, increases the lifetime performance of agents by sharing learning experiences between all agents in the society. In this paper, the design problem is decomposed into two different parts: 1) developing a repertoire of behavior modules and 2) organizing them in the agent's architecture. Our proposed cooperative co-evolutionary approach solves the first problem by evolving behavior modules in their separate genetic pools. We address the problem of relating the fitness of the agent to the fitness of behavior modules by proposing two fitness sharing mechanisms, namely uniform and value-based fitness sharing mechanisms. The organization of behavior modules in the architecture is determined by our structure learning method. A mathematical formulation is provided that shows how to decompose the value of the structure into simpler components. These values are estimated during learning and are used to find the organization of behavior modules during the agent's lifetime. To accelerate the learning process, we introduce a culture-based method based on our new interpretation of the meme metaphor. Our proposed memetic algorithm is a mechanism for sharing learned structures among agents in the society. Lifetime performance of the agent, which is quite im- - portant for real-world applications, increases considerably when the memetic algorithm is in action. Finally, we apply our methods to two benchmark problems: an abstract problem and a decentralized multirobot object-lifting task, and we achieve human-competitive architecture designs. View full abstract»

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  • On Set-Based Multiobjective Optimization

    Page(s): 58 - 79
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1080 KB) |  | HTML iconHTML  

    Assuming that evolutionary multiobjective optimization (EMO) mainly deals with set problems, one can identify three core questions in this area of research: 1) how to formalize what type of Pareto set approximation is sought; 2) how to use this information within an algorithm to efficiently search for a good Pareto set approximation; and 3) how to compare the Pareto set approximations generated by different optimizers with respect to the formalized optimization goal. There is a vast amount of studies addressing these issues from different angles, but so far only a few studies can be found that consider all questions under one roof. This paper is an attempt to summarize recent developments in the EMO field within a unifying theory of set-based multiobjective search. It discusses how preference relations on sets can be formally defined, gives examples for selected user preferences, and proposes a general preference-independent hill climber for multiobjective optimization with theoretical convergence properties. Furthermore, it shows how to use set preference relations for statistical performance assessment and provides corresponding experimental results. The proposed methodology brings together preference articulation, algorithm design, and performance assessment under one framework and thereby opens up a new perspective on EMO. View full abstract»

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  • Bi-Objective Multipopulation Genetic Algorithm for Multimodal Function Optimization

    Page(s): 80 - 102
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1027 KB) |  | HTML iconHTML  

    This paper describes the latest version of a bi-objective multipopulation genetic algorithm (BMPGA) aiming to locate all global and local optima on a real-valued differentiable multimodal landscape. The performance of BMPGA is compared against four multimodal GAs on five multimodal functions. BMPGA is distinguished by its use of two separate but complementary fitness objectives designed to enhance the diversity of the overall population and exploration of the search space. This is coupled with a multipopulation and clustering scheme, which focuses selection within the various sub-populations and results in effective identification and retention of the optima of the target functions as well as improved exploitation within promising areas. The results of the empirical comparison provide clear evidence that supports the conclusion that BMPGA is better than the other GAs in terms of overall effectiveness, applicability, and reliability. The practical value of BMPGA has already been demonstrated in applications to multiple ellipses and elliptic objects detection in microscopic imagery. View full abstract»

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  • Grouping Genetic Algorithm for the Blockmodel Problem

    Page(s): 103 - 111
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (236 KB) |  | HTML iconHTML  

    Many areas of research examine the relationships between objects. A subset of these research areas focuses on methods for creating groups whose members are similar based on some specific attribute(s). The blockmodel problem has as its objective to group objects in order to obtain a small number of large groups of similar nodes. In this paper, a grouping genetic algorithm (GGA) is applied to the blockmodel problem. Testing on numerous examples from the literature indicates a GGA is an appropriate tool for solving this type of problem. Specifically, our GGA provides good solutions, even to large-size problems, in reasonable computational time. View full abstract»

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  • HCS: A New Local Search Strategy for Memetic Multiobjective Evolutionary Algorithms

    Page(s): 112 - 132
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (888 KB) |  | HTML iconHTML  

    In this paper, we propose and investigate a new local search strategy for multiobjective memetic algorithms. More precisely, we suggest a novel iterative search procedure, known as the Hill Climber with Sidestep (HCS), which is designed for the treatment of multiobjective optimization problems, and show further two possible ways to integrate the HCS into a given evolutionary strategy leading to new memetic (or hybrid) algorithms. The pecularity of the HCS is that it is intended to be capable both moving toward and along the (local) Pareto set depending on the distance of the current iterate toward this set. The local search procedure utilizes the geometry of the directional cones of such optimization problems and works with or without gradient information. Finally, we present some numerical results on some well-known benchmark problems, indicating the strength of the local search strategy as a standalone algorithm as well as its benefit when used within a MOEA. For the latter we use the state of the art algorithms Nondominated Sorting Genetic Algorithm-II and Strength Pareto Evolutionary Algorithm 2 as base MOEAs. View full abstract»

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  • Customizable FPGA IP Core Implementation of a General-Purpose Genetic Algorithm Engine

    Page(s): 133 - 149
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1222 KB) |  | HTML iconHTML  

    Hardware implementation of genetic algorithms (GAs) is gaining importance because of their proven effectiveness as optimization engines for real-time applications (e.g., evolvable hardware). Earlier hardware implementations suffer from major drawbacks such as absence of GA parameter programmability, rigid predefined system architecture, and lack of support for multiple fitness functions. In this paper, we report the design of an IP core that implements a general-purpose GA engine that addresses these problems. Specifically, the proposed GA IP core can be customized in terms of the population size, number of generations, crossover and mutation rates, random number generator seed, and the fitness function. It has been successfully synthesized and verified on a Xilinx Virtex II Pro Field programmable gate arrays device (xc2vp30-7ff896) with only 13% logic slice utilization, 1% block memory utilization for GA memory, and a clock speed of 50 MHz. The GA core has been used as a search engine for real-time adaptive healing but can be tailored to any given application by interfacing with the appropriate application-specific fitness evaluation module as well as the required storage memory and by programming the values of the desired GA parameters. The core is soft in nature i.e., a gate-level netlist is provided which can be readily integrated with the user's system. The performance of the GA core was tested using standard optimization test functions. In the hardware experiments, the proposed core either found the globally optimum solution or found a solution that was within 3.7% of the value of the globally optimal solution. The experimental test setup including the GA core achieved a speedup of around 5.16?? over an analogous software implementation. View full abstract»

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  • Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology

    Page(s): 150 - 169
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1413 KB) |  | HTML iconHTML  

    Niching is an important technique for multimodal optimization. Most existing niching methods require specification of certain niching parameters in order to perform well. These niching parameters, often used to inform a niching algorithm how far apart between two closest optima or the number of optima in the search space, are typically difficult to set as they are problem dependent. This paper describes a simple yet effective niching algorithm, a particle swarm optimization (PSO) algorithm using a ring neighborhood topology, which does not require any niching parameters. A PSO algorithm using the ring topology can operate as a niching algorithm by using individual particles' local memories to form a stable network retaining the best positions found so far, while these particles explore the search space more broadly. Given a reasonably large population uniformly distributed in the search space, PSO algorithms using the ring topology are able to form stable niches across different local neighborhoods, eventually locating multiple global/local optima. The complexity of these niching algorithms is only O(N), where N is the population size. Experimental results suggest that PSO algorithms using the ring topology are able to provide superior and more consistent performance over some existing PSO niching algorithms that require niching parameters. View full abstract»

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  • Special issue on Evolving developmental Systems

    Page(s): 170
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  • Special issue on Advances in Memetic Computation

    Page(s): 171
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  • Why we joined ... [advertisement]

    Page(s): 172
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  • IEEE Computational Intelligence Society Information

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

    Page(s): C4
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Aims & Scope

The IEEE Transactions on Evolutionary Computation publishes high-quality technical papers in the application, design, and theory of evolutionary computation: Readers are encouraged to submit papers that disclose significant technical and practical knowledge, exploratory developments and applications of evolutionary computation. Emphasis is given to engineering systems and scientific applications. The Transactions also contains a letters section, which includes information of current interest as well as comments and rebuttals submitted in connection with published papers.  Representative applications areas include the following aspects of evolutionary computation: 1. Evolutionary optimization particularly with constraints; 2. Machine learning; 3. Intelligent systems design; 4. Image processing. machine vision; 5. Pattern recognition; 6. Evolutionary neurocomputing; 7. Evolutionary fuzzy systems; 8. Applications in biomedicine and biochemistry; 9. Robotics and control; 10. Mathematical modeling; 11. Civil, chemical, aeronautical, and industrial engineering applications.

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Meet Our Editors

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