Evolutionary Computation最新文献

In real-world applications, users often favor structurally diverse design choices over one high-quality solution. It is therefore important to consider more solutions that decision makers can compare and further explore based on additional criteria. Alongside the existing approaches of evolutionary diversity optimization, quality diversity, and multimodal optimization, this paper presents a fresh perspective on this challenge by considering the problem of identifying a fixed number of solutions with a pairwise distance above a specified threshold while maximizing their average quality. We obtain first insight into these objectives by performing a subset selection on the search trajectories of different well-established search heuristics, whether they have been specifically designed with diversity in mind or not. We emphasize that the main goal of our work is not to present a new algorithm but to understand the capability of off-the-shelf algorithms to quantify the trade-off between the minimum pairwise distance within batches of solutions and their average quality. We also analyze how this trade-off depends on the properties of the underlying optimization problem. A possibly surprising outcome of our empirical study is the observation that naive uniform random sampling establishes a very strong baseline for our problem, hardly ever outperformed by the search trajectories of the considered heuristics. We interpret these results as a motivation to develop algorithms tailored to produce diverse solutions of high average quality.

In many recent works, the potential of Exploratory Landscape Analysis (ELA) features to numerically characterize single-objective continuous optimization problems has been demonstrated. These numerical features provide the input for all kinds of machine learning tasks in the domain of continuous optimization problems, ranging, i.a., from High-level Property Prediction to Automated Algorithm Selection and Automated Algorithm Configuration. Without ELA features, analyzing and understanding the characteristics of single-objective continuous optimization problems is-to the best of our knowledge-very limited. Yet, despite their usefulness, as demonstrated in several past works, ELA features suffer from several drawbacks. These include, in particular, (1) a strong correlation between multiple features, as well as (2) its very limited applicability to multiobjective continuous optimization problems. As a remedy, recent works proposed deep learning-based approaches as alternatives to ELA. In these works, among others point-cloud transformers were used to characterize an optimization problem's fitness landscape. However, these approaches require a large amount of labeled training data. Within this work, we propose a hybrid approach, Deep-ELA, which combines (the benefits of) deep learning and ELA features. We pre-trained four transformers on millions of randomly generated optimization problems to learn deep representations of the landscapes of continuous single- and multiobjective optimization problems. Our proposed framework can either be used out-of-the-box for analyzing single- and multi-objective continuous optimization problems, or subsequently fine-tuned to various tasks focusing on algorithm behavior and problem understanding.

Extending state-of-the-art evolutionary algorithms is a widespread research direction. This trend has resulted in algorithms that give good results but are complex and challenging to analyze. One of these algorithms is EA4Eig - the winner of the CEC 2022 competition on single objective bound constrained search. The algorithm internally uses four optimization algorithms with modified components. This paper presents an analysis of EA4Eig and proposes a simplified version thereof exhibiting better optimization performance. The analysis found that the original source code contains errors that impact the algorithm's rank. The code was corrected, and the CEC 2022 competition ranking was recalculated. The impact of individual EA4Eig components on its performance was empirically analyzed. As a result, the algorithm was simplified by removing two of them. The best remaining component was analyzed further, which made it possible to remove some unnecessary and harmful code. Several versions of the algorithm were created and tested, varying in the degree of simplification. The simplest of them is implemented in 244 lines of C++ code, whereas the original implementation used 716 lines of Matlab code. Further analyses focused on the parameters of the algorithm. The constants hidden in the source code were named and treated as additional configurable parameters that underwent tuning. The ablation analyses showed that two of these hidden parameters had the most significant impact on the improvement achieved by the tuned version. The results of the original and simplified versions were compared on CEC 2022 and BBOB benchmarks. The results confirm that the simplified version is better than the original one on both these benchmarks.

Dynamic flexible job shop scheduling (DFJSS) is an important combinatorial optimisation problem, requiring simultaneous decision-making for machine assignment and operation sequencing in dynamic environments. Genetic programming (GP), as a hyper-heuristic approach, has been extensively employed for acquiring scheduling heuristics for DFJSS. A drawback of GP for DFJSS is that GP has weak exploration ability indicated by its quick diversity loss during the evolutionary process. This paper proposes an effective GP algorithm with tabu lists to capture the information of explored areas and guide GP to explore more unexplored areas to improve GP's exploration ability for enhancing GP's effectiveness. First, we use phenotypic characterisation to represent the behaviour of tree-based GP individuals for DFJSS as vectors. Then, we build tabu lists that contain phenotypic characterisations of explored individuals at the current generation and across generations, respectively. Finally, newly generated offspring are compared with the individuals' phenotypic characterisations in the built tabu lists. If an individual is unseen in the tabu lists, it will be kept to form the new population at the next generation. Otherwise, it will be discarded. We have examined the proposed GP algorithm in nine different scenarios. The findings indicate that the proposed algorithm outperforms the compared algorithms in the majority of scenarios. The proposed algorithm can maintain a diverse and well-distributed population during the evolutionary process of GP. Further analyses show that the proposed algorithm does cover a large search area to find effective scheduling heuristics by focusing on unseen individuals.

One of the most common problems in data mining applications is the uneven distribution of classes, which appears in many real-world scenarios. The class of interest is often highly underrepresented in the given dataset, which harms the performance of most classifiers. One of the most successful methods for addressing the class imbalance problem is to oversample the minority class using synthetic samples. Since the original algorithm, the synthetic minority oversampling technique (SMOTE), introduced this method, numerous versions have emerged, each of which is based on a specific hypothesis about where and how to generate new synthetic instances. In this paper, we propose a different approach based exclusively on evolutionary computation that imposes no constraints on the creation of new synthetic instances. Majority class undersampling is also incorporated into the evolutionary process. A thorough comparison involving three classification methods, 85 datasets, and more than 90 class-imbalance strategies shows the advantages of our proposal.