Evolutionary Computation Conference Research Articles

Special issue on parallel architectures and bioinspired algorithm: guest editors message

This special issue includes the extended version of selected papers presented at the fourth Parallel Architectures and Bioinspired Algorithms Workshop held in Galveston Island (TX, USA) on October 14, 2011 in conjunction with Parallel Architectures and Compilation Techniques (PACT). This workshop follows the success of the three previous workshops held in conjunction with PACT 2008 in Toronto, Canada [1], PACT 2009 in Raleigh, USA [2], and PACT 2010 in Vienna [3], Austria and the two previous Workshops on Parallel Bioinspired Algorithms [4] held in Oslo, 2005, (together with IEEE International Conference on Parallel Processing (ICPP)) [5] and London, 2007 (together with Association for Computing Machinery (ACM) Genetic and Evolutionary Computation Conference (Gecco) 2007) [6]. These series of workshops has shown that knowledge fields such as parallel computer architectures and Parallel and Distributed Computing and Bioinspired Algorithms, which could seem quite different in a first approach, are able to find transversal elements that enrich them. Bioinspired Algorithms comprise a set of heuristics that can help to optimize a wide range of problems, including many tasks faced by parallel architectures designers, such as balancing computer load, fault-tolerance and dependability, thermal-aware design, and NoC design. In addition, Bioinspired Algorithms may help in finding solutions related to compilation and resource sharing issues, which are interesting problems for parallel architectures. Parallel architecture designers may also propose infrastructures that allow the improvement of computing performance of Bioinspired Algorithms, which usually face real-world problems that manage huge amounts of data. Alternatives to the classical sequential solution are needed by this community. Therefore, topics such as P2P, cluster and grid computing, cloud computing, and Graphics Processing Unit (GPUs) implementation of Bioinspired Algorithms are very interesting to this field. We have therefore considered this opportunity to give a broader view on the application of nature inspired computing techniques to hardware design and parallel architectures problem solving. We have also considered the interest of including an overview of the available bioinspired techniques based tools that have been used so far to solve problems related to automatic hardware design. We thus invited Professors Oscar Garnica and Juan Lanchares to work with us with this aim. Therefore, this special issue includes the paper entitled A review of bioinspired CAD tools for parallel architectures and hardware design [7] together with four papers carefully selected for

Benchmarking of Continuous Black Box Optimization Algorithms

Benchmarking of optimization algorithms is necessary to quantitatively assess the performance of optimizers and to understand their strengths and weaknesses. The Black Box Optimization Benchmarking (BBOB) workshops that took place in 2009, 2010, and 2012 during the Genetic and Evolutionary Computation Conference (GECCO) were set up to benchmark both stochastic and deterministic continuous optimization algorithms. For this purpose, a thorough experimental setting, a set of test functions, and a visualization tool were designed and provided. They are based on the idea that (i) test functions should be representative of typical known difficulties, scalable with dimension, and not too easy to solve, yet comprehensible; and (ii) performance measures should be quantitative. A tool for acquiring and postprocessing data was provided.

Theory of Randomized Search Heuristics

Randomized search heuristics such as evolutionary algorithms, evolution strategies, ant colony optimizers etc. are optimization algorithms that can be applied to a wide class of problems ranging from combinatorial to continuous optimization. They are popular in practice because they are generally easy to implement, their application requires little assumptions on the function to be optimized and provide solutions to a problem in a comparatively short time. Their theoretical analysis has gained momentum over the past decade. Runtime analysis of various randomized search heuristics, using and adapting methods from the analysis of randomized algorithms, has emerged as a new line of research, and general theoretical tools that can be applied to different algorithms have been developed. In 2007, a theory track dedicated to analyses of this kind was established at the Genetic and Evolutionary Computation Conference (GECCO), one of the largest conference on randomized search heuristics. This special issue on Theory of Randomized Search Heuristics of Algorithmica contains papers that are extensions or follow-ups of papers from the theory track of the GECCO held July 7–11, 2010 in Portland, Oregon. Each article has undergone a new rigorous journal review process from which four papers have been selected. They contribute to improve our theoretical knowledge about randomized search heuristics as well as they enhance general tools and techniques for their analysis. The paper Black-Box Search by Unbiased Variation by Lehre and Witt (doi:10. 1007/s00453-012-9616-8) reconsiders and refines the black-box model of randomized search heuristics originally proposed by Droste, Jansen and Wegener (Theory

Guest editorial: special issue on selected papers from the European conference on genetic programming.

Guest editorial: special issue on selected papers from the European conference on genetic programming.

Congratulations to Japanese Society for Evolutionary Computation

This letter is to offer my sincere congratulations to the newly established Japanese Society for Evolutionary Computation. I would like to offer my good wishes as an individual working in the field of Evolutionary Computation. But I would also like to offer congratulations and best wishes on behalf of the ACM Special Interest Group on Evolutionary Computation (ACM-SIGEVO, which I currently Chair) and the Genetic and Evolutionary Computation Conference (GECCO). Over the last 20 years Japanese researchers have already made critical and lasting contributions to research in evolutionary computation. As early as 1991, pioneering Japanese researchers were making early, fundamental contributions to the field, as evidenced by publications in the International Conference on Genetic Algorithms in areas such as the evolution of neural network architectures, the optimization of associative memory models, job shop scheduling applications, and the use of Markov models to model evolutionary processes. Over the years, there have continued to be a number of important and seminal contributions from Japanese researchers. It is very impressive that you have 170 members and already have a strong record of organizing recent symposia. The existence of the Japanese Society for Evolutionary Computation will surely help to stimulate more research and advance the state of the art in evolutionary computation. The establishment of an academic journal in Japanese that focuses on evolutionary computation will also be of great value. Again, congratulations on the formation of a society. I look forward to many positive contributions to our field from your organization.

Complexity at large

Complexity at large

Theory of Evolutionary Computation

Evolutionary algorithms are general-purpose randomized search heuristics. They have been successfully applied to many different kinds of search and optimization problems. They are popular in practice since they are easy to implement and easy to apply, and yet often find good solutions in reasonable time. This lead to an increased interest in gaining a thorough understanding of their strengths (and limitations). In the past decades, the theory of evolutionary algorithms has become an important and accepted field of research. This special issue Theory of Evolutionary Computation of Algorithmica contains expanded research from the theory track of the Genetic and Evolutionary Computation Conference and the International Conference on Parallel Problem Solving From Nature. All papers have been enhanced, extended, and undergone a critical journal reviewing process. The result is a collection of seven papers that all deal with theoretical aspects of evolutionary computation in a rigorous and accessible way. It contributes to the development of a better understanding of evolutionary algorithms, paving the way for a more informed application of these algorithms. Ten years after the first publication of a run-time analysis of the most simple evolutionary algorithm, the (1 + 1) evolutionary algorithm, Jens Jagerskupper reconsiders the result. By using the drift analysis method introduced by He and Yao in a sophisticated way, he obtains much more precise bounds on the expected optimization time. Drift analysis is also the topic of Pietro S. Oliveto’s and Carsten Witt’s article. They present a theorem that yields exponential lower bounds on the expected op-

Complexity at large

Complexity at large

The 2009 Simulated Car Racing Championship

In this paper, we overview the 2009 Simulated Car Racing Championship-an event comprising three competitions held in association with the 2009 IEEE Congress on Evolutionary Computation (CEC), the 2009 ACM Genetic and Evolutionary Computation Conference (GECCO), and the 2009 IEEE Symposium on Computational Intelligence and Games (CIG). First, we describe the competition regulations and the software framework. Then, the five best teams describe the methods of computational intelligence they used to develop their drivers and the lessons they learned from the participation in the championship. The organizers provide short summaries of the other competitors. Finally, we summarize the championship results, followed by a discussion about what the organizers learned about 1) the development of high-performing car racing controllers and 2) the organization of scientific competitions.

DEMORS: A hybrid multi-objective optimization algorithm using differential evolution and rough set theory for constrained problems

The aim of this paper is to show how the hybridization of a multi-objective evolutionary algorithm (MOEA) and a local search method based on the use of rough set theory is a viable alternative to obtain a robust algorithm able to solve difficult constrained multi-objective optimization problems at a moderate computational cost. This paper extends a previously published MOEA [Hernández-Díaz AG, Santana-Quintero LV, Coello Coello C, Caballero R, Molina J. A new proposal for multi-objective optimization using differential evolution and rough set theory. In: 2006 genetic and evolutionary computation conference (GECCO’2006). Seattle, Washington, USA: ACM Press; July 2006], which was limited to unconstrained multi-objective optimization problems. Here, the main idea is to use this sort of hybrid approach to approximate the Pareto front of a constrained multi-objective optimization problem while performing a relatively low number of fitness function evaluations. Since in real-world problems the cost of evaluating the objective functions is the most significant, our underlying assumption is that, by aiming to minimize the number of such evaluations, our MOEA can be considered efficient. As in its previous version, our hybrid approach operates in two stages: in the first one, a multi-objective version of differential evolution is used to generate an initial approximation of the Pareto front. Then, in the second stage, rough set theory is used to improve the spread and quality of this initial approximation. To assess the performance of our proposed approach, we adopt, on the one hand, a set of standard bi-objective constrained test problems and, on the other hand, a large real-world problem with eight objective functions and 160 decision variables. The first set of problems are solved performing 10,000 fitness function evaluations, which is a competitive value compared to the number of evaluations previously reported in the specialized literature for such problems. The real-world problem is solved performing 250,000 fitness function evaluations, mainly because of its high dimensionality. Our results are compared with respect to those generated by NSGA-II, which is a MOEA representative of the state-of-the-art in the area.

GECCO-2008 new contest problem!

In conjunction with the 2008 Genetic and Evolutionary Computation Conference (GECCO), Parabon Computation, the leading on-demand computation utility, announced today that it will sponsor a $1,000 prize competition in which contestants are challenged to evolve a program that can solve an arbitrarily scrambled Rubik's Cube. The competition is designed to demonstrate the combined capability of two complementary computing technologies provided by Parabon Computation. The Origin(tm) Evolutionary SDK (software development kit) uses computer-based evolutionary processes to derive (evolve) desired results and the Frontier Grid Platform harnesses the computational power of thousands of computers to deliver supercomputing as a service.

The Visual Scoring of Sleep in Adults

The 1968 Rechtschaffen and Kales (R & K) sleep scoring manual was published 15 years after REM sleep was discovered. Advances in the ensuing 28 years warranted a re-look at visual scoring of sleep stages. This paper describes the work of the AASM Visual Scoring Task Force, including methodology, a literature review and the rationale behind the new rules. Reliability studies of R & K scoring were reviewed; reliability was low for stage one and moderate for slow wave sleep. Evidence indicated that K complexes and slow waves are expressed maximal frontally, spindles centrally and alpha rhythm over the occipital region. Three derivations of EEG, two of electro-oculography, and one of chin EMG were recommended. Scoring by 30-second epochs was retained. New terminology for sleep stages was proposed. Attenuation of alpha rhythm was determined to be the most valid electrophysiological marker of sleep onset. Alternative measures were proposed for non-alpha generating subjects. K complexes associated with arousals were determined to be insufficient alone to define the new stage N2. No evidence was found to justify dividing slow wave sleep into two stages. No reasons were found to alter the current slow wave amplitude criteria at any age. The phenomena of REM sleep were defined. The rules for defining onset and termination of REM sleep periods were simplified. Movement time was eliminated and major body movements defined. Studies are needed to test the reliability of the new rules. Future advances in technology may require modification of these rules with time.

Fitness inheritance in multiple objective evolutionary algorithms: A test bench and real-world evaluation

In many real-world applications of evolutionary algorithms, the fitness of an individual has to be derived using complex models and time-consuming computations. Especially in the case of multiple objective optimisation problems, the time needed to evaluate these individuals increases exponentially with the number of objectives due to the ‘curse of dimensionality’ [J. Chen, D.E. Goldberg, S. Ho, K. Sastry, Fitness inheritance in multi-objective optimization, in: W.B. Langdon et al. (Eds.), GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, July 9–13, Morgan Kaufmann Publishers, New York, 2002, pp. 319–326]. This in turn leads to a slower convergence of the evolutionary algorithms. It is not feasible to use time-consuming models with large population sizes unless the time to evaluate the objective functions is reduced. Fitness inheritance is an efficiency enhancement technique that was originally proposed by Smith et al. [R.E. Smith, B.A. Dike, S.A. Stegmann, Fitness inheritance in genetic algorithms, in: Proceedings of the 1995 ACM Symposium on Applied Computing, February 26–28, ACM, Nashville, TN, USA, 1995] to improve the performance of genetic algorithms. Sastry et al. [K. Sastry, D.E. Goldberg, M. Pelikan, Don’t evaluate, inherit, in: L. Spector et al. (Eds.), GECCO 2001: Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann Publishers, San Francisco, 2001, pp. 551–558] and Chen et al. [J. Chen, D.E. Goldberg, S. Ho, K. Sastry, Fitness inheritance in multi-objective optimization, in: W.B. Langdon et al. (Eds.), GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, July 9–13, Morgan Kaufmann Publishers, New York, 2002, pp. 319–326] have developed analytical models for fitness inheritance. In this paper, the usefulness of fitness inheritance for a set of popular and separable multiple objective test functions as well as a non-separable real-world problem is evaluated based on unary performance measures testing closeness to the Pareto-optimal front, uniform distribution along and extent of the obtained Pareto front. A statistical evaluation of the performance of an NSGA-II like algorithm on the basis of these unary performance measures suggests that especially for non-convex or non-continuous problems the use of fitness inheritance negatively affects the closeness to the Pareto-optimal front.

Circular Navigation Missile Guidance with Incomplete Information and Uncertain Autopilot Model

4Goldberg, D., Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley Longman, Reading, MA, 1989, p. 22. 5Isaacs, R., Differential Games, Wiley, New York, 1965, p. 279. 6Horie, K., “Collocation with Nonlinear Programming for Two-Sided Flight Path Optimization,” Ph.D. Dissertation, Dept. of Aeronautical and Astronautical Engineering, Univ. of Illinois at Urbana–Champaign, Urbana, IL, Feb. 2002. 7Goldberg, D. E., and Voessner, S., “Optimizing Global-Local Search Hybrids,” Proceedings of the Genetic and Evolutionary Computation Conference 1999, Morgan Kaufmann, San Francisco, 1999.

D-Ants: Savings Based Ants divide and conquer the vehicle routing problem

This paper presents an algorithm that builds on the Savings based Ant System presented in [Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), Morgan Kaufmann, San Francisco, 2002] and enhances its performance in terms of computational effort. This is achieved by decomposing the problem and solving only the much smaller subproblems resulting from the decomposition. The computational study and statistical analysis conducted both on standard benchmark problem instances as well as on new large scale Vehicle Routing Problem instances will show that the approach does not only improve the efficiency, but also improves the effectiveness of the algorithm leading to a fast and powerful problem solving tool for real world sized Vehicle Routing Problems.

Editorial Introduction.

There is exciting news! The International Society for Genetic Algorithms (ISGA) and Genetic Programming, Inc. have joined efforts, recombined, and organized a new society: The International Society for Genetic and Evolutionary Computation (ISGEC). This follows the merger of the International Conference on Genetic Algorithms and the Genetic Programming Conference into a single conference: The Genetic and Evolutionary Computation Conference (GECCO). This is exciting because the new society will have dues that include a subscription to Evolutionary Computation as well as the new journal Genetic Programming and Evolvable Machines. This will help to link Evolutionary Computation more directly to the community that founded it and to associate the journal with a large body of researchers and application developers that would appear to be its natural readership. The journal Evolutionary Computation was founded as a grassroots effort and with a spirit of cooperation that embraced all aspects of the field. The new society builds on this theme.

Conference Report

GECCO-99 STUDENT WORKSHOP(13th July, 1999)The Genetic and Evolutionary Computation Conference (GECCO-99) in Orlando, Florida, USA was the biggest ever conference on evolutionary computation. It was a joint meeting of the long standing biannual International Conference on Genetic Algorithms (ICGA) which has been running since 1985 and the more recent annual Genetic Programming (GP) conference (which started in 1996). Before the start of each GP conference there has been a workshop for PhD students research in GP. This year Dr. O'Reilly (MIT AI lab) organized a joint graduate student workshop for research students principally concerned with any aspect of evolutionary computation, Evolvable Hardware, DNA computation, Artificial Life or Agents.