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首页> 外文会议>International conference on artificial intelligence and soft computing >Archive Management in Interactive Evolutionary Computation with Minimum Requirement for Human User's Fitness Evaluation Ability
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Archive Management in Interactive Evolutionary Computation with Minimum Requirement for Human User's Fitness Evaluation Ability

机译：交互式进化计算中的档案管理，对人类用户适应性评估能力的最低要求

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Interactive evolutionary computation (IEC) has a large potential ability as a personalized optimization technique to search for preferred solutions. In IEC, evolution of a population is driven by human user's preference through his/her subjective fitness evaluation. As a result, different solutions are obtained by different users for the same problem. One important challenge in the design of an efficient IEC algorithm is to decrease the human user's burden in fitness evaluation. We have proposed an idea of a (1+1)ES model of IEC with the minimum requirement for human user's fitness evaluation ability under the following assumptions: (ⅰ) human users can evaluate only a single solution at a time, (ⅱ) human users can remember only the previously examined single solution, (ⅲ) the evaluation result is whether the current solution is better than the previous one or not, and (ⅳ) human users can perform a prespecified number of evaluations in total. This model always has a single archive solution, which is used as the final solution when its execution is terminated. In this paper, we generalize the (1+1)ES model of IEC to a general (μ+1)ES model where μ is not a constant but a variable control parameter. More specifically, the value of μ is controlled so that only a single solution is obtained after the final generation (i.e., μ=1 at the final generation whereas μ can be more than one in the other generations) . We show how we can derive the upper bound on the value of μ at each generation from the requirement of μ=1 at the final generation and the above-mentioned four assumptions. We also examine the seaxch behavior of the (μ+1)ES model for various values of μ.
机译：交互式进化计算（IEC）作为搜索首选解决方案的个性化优化技术，具有很大的潜力。在IEC中，人群的进化是受人类用户通过其主观适应度评估的偏爱驱动的。结果，对于相同的问题，不同的用户获得了不同的解决方案。有效的IEC算法设计中的一项重要挑战是减轻人类用户在适应性评估中的负担。我们提出了一种IEC（1 + 1）ES模型的想法，该模型在以下假设下对人类用户的适应性评估能力具有最低要求：（ⅰ）人类用户一次只能评估一个解决方案，（ⅱ）人类用户只能记住以前检查过的单个解决方案，（ⅲ）评估结果是当前解决方案是否比以前的解决方案好，并且（ⅳ）人类用户总共可以执行预定次数的评估。该模型始终只有一个存档解决方案，该解决方案在执行终止时用作最终解决方案。在本文中，我们将IEC的（1 + 1）ES模型推广为通用的（μ+ 1）ES模型，其中μ不是常数而是可变的控制参数。更具体地，控制μ的值，使得在最后一代之后仅获得单个解（即，在最后一代中μ＝ 1，而在其他世代中μ可以大于一个）。我们展示了如何根据最终代的μ= 1的要求以及上述四个假设来推导每代的μ值的上限。我们还针对各种μ值检查了（μ+ 1）ES模型的seaxch行为。

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