GPEM 16(3) available

GPEM 16(3) is now available. This issue features THREE resource reviews (thanks both to the authors and to tireless Resource Review Editor Bill Langdon) and four interesting regular articles. Specifically, it contains:

“Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification”
by Rodrigo C. Barros, Márcio P. Basgalupp & André C. P. L. F. de Carvalho

“Evolutionary model building under streaming data for classification tasks: opportunities and challenges”
by Malcolm I. Heywood

“A study on Koza’s performance measures”
by David F. Barrero, Bonifacio Castaño, María D. R-Moreno & David Camacho

“Review and comparative analysis of geometric semantic crossovers”
by Tomasz P. Pawlak, Bartosz Wieloch & Krzysztof Krawiec

Software Review
“Software review: the KNIME workflow environment and its applications in genetic programming and machine learning”
by Steve O’Hagan & Douglas B. Kell

Book Review
“Patricia Vargas, Ezequiel Di Paolo, Inman Harvey, and Phil Husbands (eds), The Horizons of Evolutionary Robotics, The MIT Press, 2014, ISBN: 978-0-262-02676-5, Hardcover book, 302 pages”
by Joel Lehman

Book Review
“Angelo Cangelosi and Matthew Schlesinger: Developmental robotics”
by Lisa A. Meeden

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John H. Holland

Sad news that John Holland passed away on the weekend. A warm obituary can be found here:

http://www.santafe.edu/news/item/in-memoriam-john-holland/

Many people’s lives and research have been touched by his ideas and enthusiasm.  This site definitely would not exist without them.

Curiously, his passing may not have been major mainstream news, but his ideas are. It was interesting to note that fields with his ideas were name checked in the latest Google announcement: https://abc.xyz.

History will undoubtedly recognise John as a pioneer in the computer age.

 

 

 

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CFP: Special Issue on Genetic Improvement

Special Issue on  Genetic Improvement

Call for Papers

Guest Editor: Justyna Petke, University College London, London;  j.petke@ucl.ac.uk

Genetic Improvement is the application of evolutionary and search-based optimisation methods to the improvement of existing software. For example, it may be used to automate the process of bug-fixing or to minimise bandwidth, memory or energy use. Genetic programming can use human-written software as a feed stock for GI and is able to evolve mutant software tailored to solving particular problems. Other interesting areas are automatic software transplantation, as well as “grow-and-graft” genetic programming, where software is incubated outside its target human written code and subsequently grafted into it via genetic improvement.

Work on genetic improvement has resulted in several awards, including three “Humies”, awarded for human-competitive results. This includes the bug fixing work that led to the construction of the GenProg tool [1]. More recently, genetic improvement was able to automatically transplant new functionality into existing software [2], which resulted in a ACM SIGSOFT Distinguished Paper Award at ISSTA 2015.

Scope: We invite submissions on any aspect of genetic improvement, including, but not limited to, theoretical results and interesting new applications.  Suggested topics include automatic:

– bandwidth minimisation
– latency minimisation
– fitness optimisation
– energy optimisation
– software specialisation
– memory optimisation
– software transplantation
– bug fixing
– multi-objective optimisation
– trading between quality and non-functional properties

Important Dates:
GPEM Special Issue Submission Deadline: 19 December 2014
First Reviews: March 2015
Post Review Submission Deadline: April 2015
Acceptance Notification: June 2015
Camera-ready Paper Deadline: July 2015

Paper Submission:
Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals.

Springer offers authors, editors and reviewers of Genetic Programming and Evolvable Machines a web-enabled online manuscript submission and review system. Our online system offers authors the ability to track the review process of their manuscript with straightforward log-in and submission procedures, and it supports a wide range of submission file formats.

Manuscripts should be submitted to: http://GENP.edmgr.com.
Choose “ Genetic Improvement ” as the article type when submitting.

References:
1  “A Systematic Study of Automated Program Repair: Fixing 55 out of 105 Bugs for $8 Each” (ICSE 2012) by Claire Le Goues, Michael Dewey-Vogt, Stephanie Forrest* and Westley Weimer (University of Virginia, University of New Mexico*)

2 “Automated Software Transplantation”  (ISSTA 2015) by Earl T. Barr, Mark Harman, Yue Jia, Alexandru Marginean and Justyna Petke (University College London)

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IEEE Transactions on Evolutionary Computation information for authors

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

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Online Diversity Assessment in Evolutionary Multiobjective Optimization: A Geometrical Perspective

Many diversity metrics have been proposed for offline diversity measurement of the whole population in multiobjective optimization. Most of the existing methods require knowledge of the exact Pareto optimal front or the ideal vector. For this reason, there is no direct approach to use the diversity metrics in an online manner. In this paper we propose an online diversity metric that is inspired by the geometrical interpretation of convergence and diversity. In addition, the proposed method is able to measure the diversity loss caused by any individual in the population. This information is useful in the selection process as the algorithm can perform a diversity-preservation selection based on the measured diversity loss contributed by each individual. To demonstrate the effectiveness of the proposed metric in enhancing the diversification of the solution set, we implement the metric on the well-known multiobjective evolutionary algorithm with decomposition. The simulation results show the applicability and usability of the proposed online diversity measurement.

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IEEE Xplore Digital Library

Advertisement, IEEE.

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Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization

Many-objective optimization problems (ManyOPs) refer, usually, to those multiobjective problems (MOPs) with more than three objectives. Their large numbers of objectives pose challenges to multiobjective evolutionary algorithms (MOEAs) in terms of convergence, diversity, and complexity. Most existing MOEAs can only perform well in one of those three aspects. In view of this, we aim to design a more balanced MOEA on ManyOPs in all three aspects at the same time. Among the existing MOEAs, the two-archive algorithm (Two_Arch) is a low-complexity algorithm with two archives focusing on convergence and diversity separately. Inspired by the idea of Two_Arch, we propose a significantly improved two-archive algorithm (i.e., Two_Arch2) for ManyOPs in this paper. In our Two_Arch2, we assign different selection principles (indicator-based and Pareto-based) to the two archives. In addition, we design a new ${L} _{mathbf {p}}$ -norm-based ( ${p}~boldsymbol {<}1$ ) diversity maintenance scheme for ManyOPs in Two_Arch2. In order to evaluate the performance of Two_Arch2 on ManyOPs, we have compared it with several MOEAs on a wide range of benchmark problems with different numbers of objectives. The experimental results show that Two_Arch2 can cope with ManyOPs (up to 20 objectives) with satisfactory convergence, diversity, and complexity.

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IEEE World Congress on Computational Intelligence

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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An External Archive Guided Multiobjective Evolutionary Algorithm Based on Decomposition for Combinatorial Optimization

Domination-based sorting and decomposition are two basic strategies used in multiobjective evolutionary optimization. This paper proposes a hybrid multiobjective evolutionary algorithm integrating these two different strategies for combinatorial optimization problems with two or three objectives. The proposed algorithm works with an internal (working) population and an external archive. It uses a decomposition-based strategy for evolving its working population and uses a domination-based sorting for maintaining the external archive. Information extracted from the external archive is used to decide which search regions should be searched at each generation. In such a way, the domination-based sorting and the decomposition strategy can complement each other. In our experimental studies, the proposed algorithm is compared with a domination-based approach, a decomposition-based one, and one of its enhanced variants on two well-known multiobjective combinatorial optimization problems. Experimental results show that our proposed algorithm outperforms other approaches. The effects of the external archive in the proposed algorithm are also investigated and discussed.

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A Discrete Particle Swarm Optimization for Covering Array Generation

Software behavior depends on many factors. Combinatorial testing (CT) aims to generate small sets of test cases to uncover defects caused by those factors and their interactions. Covering array generation, a discrete optimization problem, is the most popular research area in the field of CT. Particle swarm optimization (PSO), an evolutionary search-based heuristic technique, has succeeded in generating covering arrays that are competitive in size. However, current PSO methods for covering array generation simply round the particle’s position to an integer to handle the discrete search space. Moreover, no guidelines are available to effectively set PSOs parameters for this problem. In this paper, we extend the set-based PSO, an existing discrete PSO (DPSO) method, to covering array generation. Two auxiliary strategies (particle reinitialization and additional evaluation of gbest) are proposed to improve performance, and thus a novel DPSO for covering array generation is developed. Guidelines for parameter settings both for conventional PSO (CPSO) and for DPSO are developed systematically here. Discrete extensions of four existing PSO variants are developed, in order to further investigate the effectiveness of DPSO for covering array generation. Experiments show that CPSO can produce better results using the guidelines for parameter settings, and that DPSO can generate smaller covering arrays than CPSO and other existing evolutionary algorithms. DPSO is a promising improvement on PSO for covering array generation.

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