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AssigneeMitsubishi Denki Kabushiki Kaisha
InventorsJ. Barry Shackleford, Etsuko Okushi, Mitsuhiro Yasuda, Takashi Iwamoto
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A multi-purpose non-problem-specific hardware-based framework for the execution of a genetic algorithm (GA) accelerates the execution speed of a GA through the implementation of hardware-based non-problem-specific functions of population memory, first and second chromosome registers, crossover module, mutation operator, and survival comparator. The non-problem-specific aspect of the hardware-based framework turns problem-specific without changing the contents of the framework once a problem-specific fitness function circuit is included for evaluating chromosomal data representing a potential problem solution. The hardware-based framework is thus applicable to a variety of problem-specific aspects of the problem-specific fitness function circuit.
A genetic algorithm (GA) machine for executing a GA using a chromosome representing a potential problem solution, said GA machine comprising:
A method for manufacturing a genetic algorithm (GA) machine for executing a GA using a chromosome representing a potential problem solution, said method comprising the step of producing hardware-based population memory, selector, crossover module, mutation operator, and survival comparator which are designed to implement a GA.
A method for producing a genetic algorithm (GA) machine for executing a GA using a chromosome representing a potential problem solution, said method comprising the steps of:
A method for executing a genetic algorithm (GA) using a chromosome representing a potential problem solution in a GA machine provided with a population memory for storing a population of chromosomes along with corresponding values of a fitness, said method comprising the step of replacing a less-fit chromosome among the chromosomes stored in the population with a more-fit child chromosome created through an execution of the GA so that a more-fit chromosome has a longer lifetime in the population memory.
A method for executing a genetic algorithm (GA) in a GA machine provided with a crossover module for creating a child chromosome through a crossover operation on a chromosome, said method comprising the step of probabilistically controlling a number of cutpoints on a chromosome used in the crossover operation based upon an externally supplied number of cutpoints.
A method for executing a genetic algorithm (GA) using a simulated chromosomal data representing a potential problem solution in a GA machine provided with a population memory for storing a population of chromosomes and corresponding values of a fitness and a fitness function circuit for evaluating the fitness of a chromosome, said method comprising the step of initializing the population memory,
Dependencies are inferred from the claim text.