Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolving hardware with genetic learning: a first step towards building a Darwin machine
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Ruggedness and neutrality—the NKp family of fitness landscapes
ALIFE Proceedings of the sixth international conference on Artificial life
Following the path of evolvable hardware
Communications of the ACM
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Evolvable Hardware in Evolutionary Robotics
Autonomous Robots
The Fast Evaluation Strategy for Evolvable Hardware
Genetic Programming and Evolvable Machines
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Dynamics of fitness sharing evolutionary algorithms for coevolution of multiple species
Applied Soft Computing
Evolvable hardware design based on a novel simulated annealing in an embedded system
Concurrency and Computation: Practice & Experience
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Evolvable hardware (EHW) has recently become a highly attractive topic of study because it offers a way of adapting hardware to a given embedded environment. However, it is not easy to evolve hardware efficiently and effectively, so many challenges continue to exist when trying to solve problems. In this paper, we propose a method that uses the speciation technique to enable diverse circuits to evolve efficiently by the process of one-step evolution. As a result of studying the landscape contained in the EHW example, we have found complicated spaces contain many peaks that can lead to deceptions when using the evolving process, and the speciation technique profits from the evolution of EHW. We also studied that the speciated hardware ensemble might be a good candidate for more complex and rigorous function. In the experiments, we applied the fitness sharing method as the speciation technique, and obtained diverse hardware modules, then ascertained the efficiency of these structures. We also show that several useful extra functions and better overall performance can be obtained by analyzing diverse circuits with the speciation technique.