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In this work, an approach for solving the job shop scheduling problem using a cultural algorithm is proposed. Cultural algorithms are evolutionary computation methods that extract domain knowledge during the evolutionary process. Additional to this extracted knowledge, the proposed approach also uses domain knowledge given a priori (based on specific domain knowledge available for the job shop scheduling problem). The proposed approach is compared with respect to a Greedy Randomized Adaptive Search Procedure (GRASP), a Parallel GRASP, a Genetic Algorithm, a Hybrid Genetic Algorithm, and a deterministic method called shifting bottleneck. The cultural algorithm proposed in this article is able to produce competitive results with respect to the two approaches previously indicated at a significantly lower computational cost than at least one of them and without using any sort of parallel processing. [ABSTRACT FROM AUTHOR]

This article introduces the circuit design problem as a synthesis procedure. An evolutionary technique denominated Genetic Programing (GP) is proposed as the main engine for the synthesis of logic circuits. This article argues that the synthesis of circuits using bottom-up procedures (such as GP) is at least as powerful as any top-down method, and that this is possible by means of the replication of a single element: the binary multiplexer. The properties of this device are described as a sound basis for the synthesis of logic circuits using GP. Several circuits are synthesized and contrasted against two design methods: the standard implementation of logic functions using multiplexers, and ordered binary decision diagrams. [ABSTRACT FROM AUTHOR]