Conway's parallel sorting algorithm
Journal of Algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Synthesis of Synchronous Sequential Logic Circuits from Partial Input/Output Sequences
ICES '98 Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware
Evolving Turing Machines from Examples
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Improving Correctness of Finite-State Machine Synthesis from Multiple Partial Input/Output Sequences
EH '99 Proceedings of the 1st NASA/DOD workshop on Evolvable Hardware
Improving evolvability of genetic parallel programming using dynamic sample weighting
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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A main branch in Evolutionary Computation is learning a system directly from input/output samples without investigating thternal behaviors of the system. Input/output samples captured from a real system are usually incomplete, biased and noisy. In order to evolve a precise system, the sample set should include a complete set of samples. Thus, a large number of samples should be used. Fitness functions being used in Evohitionary Algorithms usually based on the matched ratio of samples. Unfortunately, some of these samples may be exactly or semantically duplicated. These duplicated samples cannot be identified simply because we do not know the internal behavior of the system being evolved. This paper proposes a method to overcome this problem by using a dynamic fitness function that incorporates the contribution of each sample in the evolutionary process. Experiments on evolving Finite State Machines with Genetic Algorithms are presented to demonstrate the effect on improving the successful rate and convergent speed of the proposed method.