M. Yamamura

Publications123
h-index18
Citations1,594
Highly Influential Citations110
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Multi-parent recombination with simplex crossover in real coded genetic algorithms
TLDR
Experimental results using test functions showed SPX works well on functions having multimodality and/or epistasis with a medium number of parents: 3-parent on a low dimensional function or 4 parents on high dimensional functions.
Theoretical Analysis of Simplex Crossover for Real-Coded Genetic Algorithms
TLDR
The result of theoretical analysis and experiments shows that a performance of SPX is independent of linear coordinate transformation and that SPX always optimizes various test function efficiently when theoretical value for expansion rate, which is a parameter ofSPX, is applied.
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An Efficient Genetic Algorithm for Job Shop Scheduling Problems
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Aqueous computing: writing on molecules
Molecular computing is viewed here as a process of writing on molecules while they are dissolved in water. When DNA molecules are employed, they are used only in double stranded form and only as data…
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A functional specialization hypothesis for designing genetic algorithms
  • H. Kita, M. Yamamura
  • Computer Science
    IEEE SMC'99 Conference Proceedings. IEEE…
  • 12 October 1999
TLDR
An alternative picture that captures the search process of the GA as evolution of the probability distribution function of the population is proposed and a functional specialization hypothesis that specifies the roles of selection and crossover operators is proposed as guidelines to design GAs.
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Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward
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Power plant start-up scheduling: a reinforcement learning approach combined with evolutionary computation
TLDR
This work proposes to integrate neural network-based reinforcement learning with evolutionary computation implemented by means of Genetic Algorithms to increase on-line performance of searching an optimal or near-optimal start-up schedule during power plant operation.
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A genetic algorithm for job-shop scheduling problems using job-based order crossover
TLDR
This paper introduces a new crossover, the job based order crossover (JOX), which can preserve characteristics very well and introduces a mutation for maintaining a diversity of population without disrupting characteristics.
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Self-organizing formation algorithm for active elements
TLDR
In this algorithm, an element generates virtual springs between the neighbor element based on information of how many other elements exist in the neighborhood with a certain radius, and this kind of algorithm gives a new principle of self-organizing formation, and its simplicity will be useful for the design ofSelf-assembling nano machines in future.
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Identification and characterization of new long conserved noncoding sequences in vertebrates
TLDR
The results suggest that mutations occur with equal frequency in LCNS but are eliminated by natural selection during the course of evolution.
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