Category Archives: Genetic Programming and Evolvable Machines

Sean Luke: essentials of metaheuristics
Content Type Journal ArticlePages 333-334DOI 10.1007/s10710-011-9139-0Authors
Michael Lones, University of York, York, UK

Journal Genetic Programming and Evolvable MachinesOnline ISSN 1573-7632Print ISS… Continue reading →

Introduction: special issue on evolvable hardware challenges
Content Type Journal ArticlePages 181-182DOI 10.1007/s10710-011-9138-1Authors
Pauline C. Haddow, CRAB Lab, Department of Computer Science and Informatics, The Norwegian University of Scien… Continue reading →

Abstract  Evolvable Hardware has been a discipline for over 15 years. Its application has ranged from simple circuit design to antenna
design. However, research in the field has often been criticised for not addressing real world proble… Continue reading →

Abstract  Many large combinatorial optimization problems tackled with evolutionary algorithms often require very high computational
times, usually due to the fitness evaluation. This fact forces programmers to use clusters of computers, a co… Continue reading →

Abstract  A mapping is local if it preserves neighbourhood. In Evolutionary Computation, locality is generally described as the property
that neighbouring genotypes correspond to neighbouring phenotypes. A representation has high locality if… Continue reading →

Abstract  EMBRACE has been proposed as a scalable, reconfigurable, mixed signal, embedded hardware Spiking Neural Network (SNN) device.
EMBRACE, which is yet to be realised, targets the issues of area, power and scalability through the use o… Continue reading →

Abstract  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 … Continue reading →

Open BEAGLE: a generic framework for evolutionary computations
Content Type Journal ArticlePages 329-331DOI 10.1007/s10710-011-9135-4Authors
Dmitry Batenkov, Weizmann Institute of Science, Rehovot, Israel

Journal Genetic Programming and Evolv… Continue reading →

Abstract  We propose to utilize a formal verification algorithm to reduce the fitness evaluation time for evolutionary post-synthesis
optimization in evolvable hardware. The proposed method assumes that a fully functional digital circuit is … Continue reading →

Abstract  We propose GP-zip2, a new approach to lossless data compression based on Genetic Programming (GP). GP is used to optimally
combine well-known lossless compression algorithms to maximise data compression. GP-zip2 evolves programs wi… Continue reading →