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A fitness differential adaptive parameter controlled evolutionary algorithm with application to the design structure matrix

机译：适应性微分自适应参数控制进化算法及其在设计结构矩阵中的应用

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This paper investigates a methodology for adaptation of the mutation factor within an evolutionary algorithm by means of measuring the improvement differential between successive generations. When no improvement is obtained in an evolutionary algorithm and it has not located the global optimum, it is an indication that the algorithm may have become trapped within a local minimum or maximum. Mutation is a tool within the algorithm that is designed to assist in escaping from these local extremes. It is therefore the premise of this paper that if the preset value for mutation probability is proving insufficient to release the algorithm from entrapment in a local minima or maxima, then a temporary increase in this mutation probability may assist in freeing the algorithm and therefore increasing its chances of ultimately converging on a global optimum. In order to determine when to implement the increase in mutation probability our algorithm measures the fitness improvement between successive generations in the algorithm. When no improvement is detected for a number of successive generations the probability is increased. The design structure matrix (DSM), a scheduling tool that has previously been optimized via the application of evolutionary algorithms, has been used as a practical implementation of differential a'daptation to investigate its effectiveness in solving real world problems. Solutions provided by Todd, D. (Multiple criteria genetic algorithms in engineering design and operation, PhD thesis, Department of Marine Technology, University of Newcastle, 1997), are used to benchmark the algorithm's effectiveness.
机译：本文通过测量连续世代之间的改进差异，研究了在进化算法中适应突变因子的方法。如果在进化算法中没有获得任何改进并且尚未找到全局最优值，则表明该算法可能已陷入局部的最小值或最大值之内。变异是算法中的一种工具，旨在帮助摆脱这些局部极端情况。因此，本文的前提是，如果证明突变概率的预设值不足以使算法脱离局部最小值或最大值，则此突变概率的暂时增加可能有助于释放算法并因此增加其算法最终收敛于全局最优的机会。为了确定何时实现突变概率的增加，我们的算法在算法中测量了连续几代之间的适应性提高。如果连续几代都没有检测到改善，则概率增加。设计结构矩阵（DSM）是一种调度工具，以前已通过使用进化算法对其进行了优化，已被用作差分自适应的实际实现，以研究其解决现实问题的有效性。托德（Todd，D.）提供的解决方案（工程设计和操作中的多准则遗传算法，博士学位，纽卡斯尔大学海洋技术系，1997年）用于对算法的有效性进行基准测试。

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