This paper presents a novel genetic parallel programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, M...
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This paper presents a novel genetic parallel programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. It creates a new approach to evolving a feasible problem solution in parallel program form and then serializes it into a sequential program if required. The effectiveness and efficiency of GPP are investigated using a suite of 14 well-studied benchmark problems. Experimental results show that GPP speeds up evolution substantially.
genetic parallel programming (GPP) is a novel geneticprogramming paradigm. Based on the GPP paradigm and a local search operator - FlowMap, a logic circuit synthesizing system integrating GPP and FlowMap, a Hybridize...
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ISBN:
(纸本)9781595931863
genetic parallel programming (GPP) is a novel geneticprogramming paradigm. Based on the GPP paradigm and a local search operator - FlowMap, a logic circuit synthesizing system integrating GPP and FlowMap, a Hybridized GPP based Logic Circuit Synthesizer (HGPPLCS) is developed. To show the effectiveness of the proposed HGPPLCS, six combinational logic circuit problems are used for evaluations. Each problem is run for 50 times. Experimental results show that both the lookup table counts and the propagation gate delays of the circuits collected are better than those obtained by conventional design or evolved by GPP alone. For example, in a 6-bit one counter experiment, we obtained combinational digital circuits with 8 four-input lookup tables in 2 gate level on average. It utilizes 2 lookup tables and 3 gate levels less than circuits evolved by GPP alone.
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