Cellular automata machines: a new environment for modeling
Cellular automata machines: a new environment for modeling
Punctuated equilibria: a parallel genetic algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Random number generators: good ones are hard to find
Communications of the ACM
Parallel Random Number Generation for VLSI Systems Using Cellular Automata
IEEE Transactions on Computers
Evolving cellular automata to perform computations: mechanisms and impediments
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
An introduction to genetic algorithms
An introduction to genetic algorithms
Parallel genetic programming: a scalable implementation using the transputer network architecture
Advances in genetic programming
Parallel genetic programming and its application to trading model induction
Parallel Computing
Evolution of Parallel Cellular Machines: The Cellular Programming Approach
Evolution of Parallel Cellular Machines: The Cellular Programming Approach
Fine-Grained Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Evolving Globally Synchronized Cellular Automata
Proceedings of the 6th International Conference on Genetic Algorithms
Optimization Using Distributed Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Co-evolving Parallel Random Number Generators
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Comparing Synchronous and Asynchronous Cellular Genetic Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
On fireflies, cellular systems, and evolware
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
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Parallel evolutionary algorithms, over the past few years, have proven empirically worthwhile, but there seems to be a lack of understanding of their workings. In this paper we concentrate on cellular (fine-grained) models, our objectives being: (1) to introduce a suite of statistical measures, both at the genotypic and phenotypic levels, which are useful for analyzing the workings of cellular evolutionary algorithms; and (2) to demonstrate the application and utility of these measures on a specific example---the cellular programming evolutionary algorithm. The latter is used to evolve solutions to three distinct (hard) problems in the cellular-automata domain: density, synchronization, and random number generation. Applying our statistical measures, we are able to identify a number of trends common to all three problems (which may represent intrinsic properties of the algorithm itself, as well as a host of problem-specific features. We find that the evolutionary algorithm tends to undergo a number of phases which we are able to quantitatively delimit. The results obtained lead us to believe that the measures presented herein may prove useful in the general case of analyzing fine-grained evolutionary algorithms.