ACM SIGARCH Computer Architecture News
A novel single instruction computer architecture
ACM SIGARCH Computer Architecture News
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Computer organization and design (2nd ed.): the hardware/software interface
Computer organization and design (2nd ed.): the hardware/software interface
A Hardware Implementation of a Genetic Programming System Using FPGAs and Handel-C
Genetic Programming and Evolvable Machines
Computer
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Design of a Processing Element of a SIMD Computer for Genetic Algorithms
HPC-ASIA '97 Proceedings of the High-Performance Computing on the Information Superhighway, HPC-Asia '97
Computer Architecture
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Genetic algorithms (GA) are a well-known non-deterministic means to search a problem/solution space to find an optimal but not necessarily best solution within a reasonable time period for computationally intractable problems. The genetic algorithm uses the metaphor of natural evolution with the paramount Darwinian principle of "survival of the fittest."The genetic algorithm mirrors the evolutionary mechanism in the operations used, namely selection, mutation, crossover and inversion. The genetic algorithm is used to evolve "chromosomes" that represent possible solutions to the problem, and with each succeeding generation of chromosomes in a population, a fitter chromosome ensues.One instruction set computing is a form of minimalist computing, in which the functionality of a processor is reduced to one instruction, making possible several key improvements in processor design. A specific instruction set is then formed by the orthogonality of parameters with the one instruction. This approach leads to enhancements in processor organization and structure that include scalability of construction, efficiency of design, and simplicity of programming.The genetic algorithm can be implemented using the one instruction computer architecture. This application of one instruction computing provides a unique and natural combination as a general-purpose means to optimizing problems. Moreover, the simplified construction using one instruction elements, lends itself well to computing in alternate materials such as organic, optical, chemical, or quantum components and nano-materials. Finally, the one instruction methodology has a biologic parallel in the creation of a one instruction computer using living cells.