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A circuit designed by human often results in very complex hardware architectures, requiring a large amount of manpower and computational resources. A wider objective is used to find novel solutions to design such complex architectures so that system functionality and performance may not be compromised. Design automation using reconfigurable hardware and evolutionary algorithms (EA), such as genetic algorithm (GA), is one of the methods to tackle this issue. This concept applies the notion of Evolvable Hardware (EHW) to the problem domain such as novel design solutions and circuit optimization. EHW is a new field about the use of EA to synthesize a circuit. EA manipulates a population of individuals where each individual describes how to construct a candidate for a good circuit. Each circuit is assigned a fitness, which indicates how well a candidate satisfies the design specification. EA uses stochastic operators repeatedly to evolve new circuit configurations from existing ones, and a resultant circuit configuration will exhibit a desirable behavior. In this paper, optimum circuit design by using GA with fitness function composed of circuit complexity, power and time delay is proposed, and its effectiveness is shown by simulations.