Evolutionary Optimization of Yagi-Uda Antennas
ICES '01 Proceedings of the 4th International Conference on Evolvable Systems: From Biology to Hardware
An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
GA directed self-organized search and attack UAV swarms
Proceedings of the 38th conference on Winter simulation
A comparison of different circuit representations for evolutionary analog circuit design
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
A genetic representation for evolutionary fault recovery in Virtex FPGAs
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
Open-ended evolution to discover analogue circuits for beyond conventional applications
Genetic Programming and Evolvable Machines
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High-level analog circuit design is a complex problem domain in which evolutionary search has recently produced encouraging results. However, little is known about how to best structure evolution for these tasks. The choices of circuit representation, fitness evaluation technique, and genetic operators clearly have a profound effect on the search process. In this paper, we examine fitness evaluation by comparing the effectiveness of four fitness schedules. Three fitness schedules are dynamic { the evaluation function changes over the course of the run, and one is static. Coevolutionary search is included, and we present a method of evaluating the problem population that is conducive to multiobjective optimization. Twenty-five runs of an analog amplifier design task using each fitness schedule are presented. The results indicate that solution quality is highest with static and coevolving fitness schedules as compared to the other two dynamic schedules. We discuss these results and offer two possible explanations for the observed behavior: retention of useful information, and alignment of problem difficulty with circuit proficiency.