Archive management in interactive evolutionary computation with minimum requirement for human user's fitness evaluation ability
Archive management in interactive evolutionary computation with minimum requirement for human user's fitness evaluation ability
复制标题
对人类用户适应度评估能力要求最低的交互式进化计算中的档案管理
DOI：
10.1007/978-3-319-07173-2_31
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
and Y. Nojima
中科院分区：
文献类型：
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作者：
H. Ishibuchi;T. Sudo;and Y. Nojima
交互式进化计算（IEC）作为一种搜索首选解决方案的个性化优化技术具有巨大的潜力。在 IEC 中，群体的进化是由人类用户通过他/她的主观适应度评估的偏好驱动的。因此，对于同一个问题，不同的用户会得到不同的解决方案。设计高效 IEC 算法的一个重要挑战是减轻人类用户在健身评估方面的负担。我们提出了 IEC 的 (1+1)ES 模型的想法，其对人类用户适应度评估能力的最低要求是在以下假设下：（i）人类用户一次只能评估单个解决方案，（ii）人类用户只能记住先前检查的单个解决方案，（iii）评估结果是当前解决方案是否优于前一个解决方案，以及（iv）人类用户总共可以执行预定次数的评估。该模型始终具有单个存档解决方案，在其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模型推广为一般的(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，使得在最终一代之后仅获得单一解（即，在最后一代中μ=1，而在其他代中μ可以大于1）。我们展示了如何根据最后一代 μ=1 的要求和上述四个假设推导出每一代 μ 值的上限。我们还检查了 (μ+1)ES 模型对于不同 μ 值的搜索行为。
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 whereμis not a constant but a variable control parameter. More specifically, the value ofμis controlled so that only a single solution is obtained after the final generation (i.e.,μ=1 at the final generation whereasμcan be more than one in the other generations). We show how we can derive the upper bound on the value ofμat each generation from the requirement ofμ=1 at the final generation and the above-mentioned four assumptions. We also examine the search behavior of the (μ+1)ES model for various values ofμ.