Towards Explainable AI Algorithms via Fitness Landscape Analysis in Evolutionary Computation

研究像遗传算法这样的元启发式搜索方法在与像MAP-Elites这样的照明算法一起使用时的表现如何，以自信地从解决方案空间中选择最佳候选解决方案，这是有动机的。从这些“最佳”候选解决方案出发，重点是如何使用XAI算法解释这些解决方案，以及使用Fitness Landscapes如何很好地表示XAI算法。有研究支持将EC和map - elite结合使用，并建议可以探索它们的使用，以帮助更好地适应用户偏好。也有研究支持使用XAI和景观分析，其中一项研究发现景观总体上是中性的。中性景观可能表明模型配置中存在冗余特征，这意味着最终分析没有像预期的那样精细。因此，确保模型只包含转换所需的最相关的特征是很重要的，特别是当依赖于模型的结果时。健身景观是跟踪XAI算法生成的模型健身的一种有效方法，XAI方法也可以用于健身景观，以增强EC算法的强度和最终用户的信任。这些术语之间的相互作用为研究可以应用的新领域开辟了许多潜力，例如，这可以在医疗保健诊断工具中实现，决策可以得到与该疾病相关的许多因素的支持，这可以使用称为反事实分析[2]的XAI方法来实现。该模型的适合度可以在适合度视图中显示，该模型可以在10次模拟运行中生成。在每次运行中，可以使用MAP-Elites选择最佳候选解决方案（模型）的数量。这些解决方案将有一个相关的适应度评分，并可以绘制在适应度景观上，以加强模型在每个独立模拟中的强度，使用决策树之类的东西来确定景观分析是好是坏。这只是一种可能的场景，可以对其他场景进行建模和实验，以发现集成这些技术的潜力。目前，有许多XAI工具试图为最终用户弥补这种理解上的差距。我们感兴趣的是看到这些东西在一组不同的问题域（其中每个最终用户的需求是不同的）中如何很好地协同工作。

There lies a motivation in investigating how well meta-heuristic search methods like Genetic Algorithms perform when used alongside illumination algorithms like MAP-Elites to confidently select the best candidate solutions from the solution space. From these 'best' candidate solutions, the focus is then on how these solutions can be explained using XAI algorithms, and how well XAI algorithms can be represented using Fitness Landscapes. There is research to support the use of EC and MAP-Elites together [1] and suggests that their use can be explored to help better accommodate user preferences. There is also research that supports the use of XAI and landscape analysis, with one study finding that the landscape was neutral overall. A neutral landscape can indicate redundant features exist within model configurations, meaning that the end analysis is not as refined as it could be [3]. Therefore, it is important to ensure that the models only contain the most pertinent features that are needed for the transformation especially when there is a reliance on the outcome of the model.Fitness landscapes are a useful way to keep track of the model fitness generated by XAI algorithms, and XAI methods could also be used in fitness landscapes to reinforce the strength of the EC algorithm and the trust from the end-users. The interplay between these terms opens up a lot of potential to research new areas where this could be applied, for instance this could be implemented in healthcare diagnosis tools, the decision could be supported by a number of factors pertinent to that illness, which can be achieved using an XAI method known as Counterfactual Analysis [2]. The fitness of this model can be shown on a fitness landscape, where this model can be generated across 10 simulated runs. In each run, the number of best candidate solutions (models) could be selected using MAP-Elites. These solutions will have an associated fitness score and can be plotted on a fitness landscape to reinforce how strong the model is across each of the independent simulations, using something like decision trees to determine if the landscape analysis is good or bad. This is just one possible scenario, other scenarios can be modelled and experimented with to discover the potential of integrating these technologies. Currently, there exists a number of XAI tools that try to bridge this gap in understanding for end-users[4]. The interest will be in seeing how well these things work together for a set of different problem domains, where the requirements for each end-user will differ.