ALBERT

Description: Eureqa: software review Content Type Journal Article Category Software Review Pages 173-178 DOI 10.1007/s10710-010-9124-z Authors Renáta Dubčáková, Faculty of Safety Engineering, VŠB-Technical University of Ostrava, Ostrava, Czech Republic Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 12 Journal Issue Volume 12, Number 2

Description:    This paper focuses on two issues, first perusing the idea of algorithmic design through genetic programming (GP), and, second, introducing a novel approach for analyzing and understanding the evolved solution trees. Considering the problem of list search , we evolve iterative algorithms for searching for a given key in an array of integers, showing that both correct linear-time and far more efficient logarithmic-time algorithms can be repeatedly designed by Darwinian means. Next, we turn to the (evolved) dish of spaghetti (code) served by GP. Faced with the all-too-familiar conundrum of understanding convoluted—and usually bloated—GP-evolved trees, we present a novel analysis approach, based on ideas borrowed from the field of bioinformatics. Our system, dubbed G-PEA (GP Post-Evolutionary Analysis), consists of two parts: (1) Defining a functionality-based similarity score between expressions, G-PEA uses this score to find subtrees that carry out similar semantic tasks ; (2) Clustering similar sub-expressions from a number of independently evolved fit solutions, thus identifying important semantic building blocks ensconced within the hard-to-read GP trees. These blocks help identify the important parts of the evolved solutions and are a crucial step in understanding how they work. Other related GP aspects, such as code simplification, bloat control, and building-block preserving crossover, may be extended by applying the concepts we present. Content Type Journal Article Category Original Paper Pages 121-160 DOI 10.1007/s10710-010-9122-1 Authors Kfir Wolfson, Department of Computer Science, Ben-Gurion University, 84105 Beer-Sheva, Israel Shay Zakov, Department of Computer Science, Ben-Gurion University, 84105 Beer-Sheva, Israel Moshe Sipper, Department of Computer Science, Ben-Gurion University, 84105 Beer-Sheva, Israel Michal Ziv-Ukelson, Department of Computer Science, Ben-Gurion University, 84105 Beer-Sheva, Israel Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 12 Journal Issue Volume 12, Number 2

Description: Open BEAGLE: a generic framework for evolutionary computations Content Type Journal Article Category Software Review Pages 329-331 DOI 10.1007/s10710-011-9135-4 Authors Dmitry Batenkov, Weizmann Institute of Science, Rehovot, Israel Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 12 Journal Issue Volume 12, Number 3

Description:    Interference in wireless networks is undesirable, whether it is due to unintentional or malicious causes. Adaptive beamforming is a spatial filtering technique that can prevent jammers from disrupting wireless networks. This paper presents an evolvable hardware (EH) application in which an evolutionary algorithm (EA) is used to configure an adaptive beamformer to achieve two goals: (1) steering nulls towards jamming signals and (2) directing gain in the direction of the desired signal. This is the first demonstration of an EA-configured adaptive beamformer to counter a jamming system. Simulation results show that the EA is able to thwart up to three jamming signals. The results suggest that EH is a promising approach towards wireless network security. Content Type Journal Article Pages 217-234 DOI 10.1007/s10710-011-9134-5 Authors Jason D. Lohn, Department of Electrical and Computer Engineering, Carnegie Mellon University, Silicon Valley Campus, Mountain View, CA, USA Jonathan M. Becker, Department of Electrical and Computer Engineering, Carnegie Mellon University, Silicon Valley Campus, Mountain View, CA, USA Derek S. Linden, X5 Systems Inc, Ashburn, VA, USA Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 12 Journal Issue Volume 12, Number 3

Description: Sean Luke: essentials of metaheuristics Content Type Journal Article Category Book Review Pages 333-334 DOI 10.1007/s10710-011-9139-0 Authors Michael Lones, University of York, York, UK Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 12 Journal Issue Volume 12, Number 3

Description: Justin Lee: Morphogenetic Evolvable Hardware Content Type Journal Article Category Book Review Pages 79-80 DOI 10.1007/s10710-010-9116-z Authors Martin A. Trefzer, University of York, York, UK Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 12 Journal Issue Volume 12, Number 1

Description:    The journal and in particular the resource reviews have been running for 10 years. There are a number of activities being planned to celebrate. However it is a good time to revisit our original and updated goals again [(Langdon, Genet Progrm Evolvable Mach 1(1/2):165–169 ( 2000 ); Langdon and Gustafson, Genet Program Evolvable Mach 6(2):221–228 ( 2005 )], compare them with what the journal has achieved and make new plans. "Books" section onwards gives up to date statistics on the genetic programming and evolvable hardware literature and electronic resources. Content Type Journal Article Category Resource Review Pages 321-338 DOI 10.1007/s10710-010-9111-4 Authors W. B. Langdon, King’s College London Department of Computer Science London WC2R 2LS UK S. M. Gustafson, GE Global Research Niskayuna NY 12309 USA Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 11 Journal Issue Volume 11, Numbers 3-4

Description:    This paper concerns redundancies in representation of linear genetic programming (GP). We identify the causes of redundancies in linear GP and propose a canonical transformation that converts original linear representations into a canonical form in which structural redundancies are removed. In canonical form, we can easily verify whether two representations represent an identical program. We then discuss exploitation of the proposed canonical transformation, and demonstrate a way to improve search performance of linear GP by avoiding redundant individuals. Experiments were conducted with an image feature synthesis problem. Firstly, we have verified that there are really a lot of redundancies in conventional linear GP. We then investigate the effect of avoiding redundant individuals. The results yield that linear GP with avoidance of redundant individuals obviously outperforms conventional linear GP. Content Type Journal Article Category Contributed Article Pages 49-77 DOI 10.1007/s10710-010-9118-x Authors Ukrit Watchareeruetai, Department of Media Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603 Japan Yoshinori Takeuchi, Department of Media Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603 Japan Tetsuya Matsumoto, Department of Media Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603 Japan Hiroaki Kudo, Department of Media Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603 Japan Noboru Ohnishi, Department of Media Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603 Japan Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 12 Journal Issue Volume 12, Number 1

Description:    Genetic programming has now been used to produce at least 76 instances of results that are competitive with human-produced results. These human-competitive results come from a wide variety of fields, including quantum computing circuits, analog electrical circuits, antennas, mechanical systems, controllers, game playing, finite algebras, photonic systems, image recognition, optical lens systems, mathematical algorithms, cellular automata rules, bioinformatics, sorting networks, robotics, assembly code generation, software repair, scheduling, communication protocols, symbolic regression, reverse engineering, and empirical model discovery. This paper observes that, despite considerable variation in the techniques employed by the various researchers and research groups that produced these human-competitive results, many of the results share several common features. Many of the results were achieved by using a developmental process and by using native representations regularly used by engineers in the fields involved. The best individual in the initial generation of the run of genetic programming often contains only a small number of operative parts. Most of the results that duplicated the functionality of previously issued patents were novel solutions, not infringing solutions. In addition, the production of human-competitive results, as well as the increased intricacy of the results, are broadly correlated to increased availability of computing power tracked by Moore’s law. The paper ends by predicting that the increased availability of computing power (through both parallel computing and Moore’s law) should result in the production, in the future, of an increasing flow of human-competitive results, as well as more intricate and impressive results. Content Type Journal Article Category Contributed Article Pages 251-284 DOI 10.1007/s10710-010-9112-3 Authors John R. Koza, Stanford University Department of Electrical Engineering Stanford CA 94305 USA Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 11 Journal Issue Volume 11, Numbers 3-4

Description: Arthur K. Kordon: Applying computational intelligence: how to create value Content Type Journal Article Category Book Review Pages 85-86 DOI 10.1007/s10710-010-9120-3 Authors Guillermo Leguizamón, Laboratorio de Investigación y Desarrollo en Inteligencia Computacional, Universidad Nacional de San Luis, San Luis, Argentina Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 12 Journal Issue Volume 12, Number 1