Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
A parallel island model genetic algorithm for the multiprocessor scheduling problem
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Principles in the Evolutionary Design of Digital Circuits—Part I
Genetic Programming and Evolvable Machines
Hybrid Distributed Real-Coded Genetic Algorithms
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Proceedings of the European Conference on Genetic Programming
Studying the Influence of Communication Topology and Migration on Distributed Genetic Programming
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Dynamic Programming
Parallel Genetic Programming on a Network of Transputers
Parallel Genetic Programming on a Network of Transputers
Comparing Different Serial and Parallel Heuristics to Design Combinational Logic Circuits
EH '03 Proceedings of the 2003 NASA/DoD Conference on Evolvable Hardware
An analysis of island models in evolutionary computation
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Genetic parallel programming: design and implementation
Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Designing digital circuits for FPGAs using parallel genetic algorithms (WIP)
Proceedings of the 2012 Symposium on Theory of Modeling and Simulation - DEVS Integrative M&S Symposium
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Evolutionary Electronics (EE) is a research area which involves application of Evolutionary Computation in the domain of electronics. EE algorithms are generally able to find good solutions to rather small problems in a reasonable amount of time, but the need for solving more and more complex problems increases the time required to find adequate solutions. This is due to the large number of individuals to be evaluated and to the large number of generations required until the convergence process leads to the solution. As a consequence, there have been multiple efforts to make EE faster, and one of the most promising choices is to use distributed implementations. In this paper, we propose a cluster-based evolutionary design of digital circuits using a distributed improved multi expression programming method (DIMEP). DIMEP keeps, in parallel, several sub-populations that are processed by Impoved Multi-Expression Programming algorithms, with each one being independent from the others. A migration mechanism produces a chromosome exchange between the subpopulations using MPI (Message Passing Interface) on a dedicated cluster of workstations (Lido Cluster, Dortmund University). This paper presents the main ideas and shows preliminary experimental results.