The importance of explainability in AI has become a pressing concern, for which several explainable AI (XAI) approaches have been recently proposed. However, most of the available XAI techniques are post-hoc methods, which however may be only partially reliable, as they do not reflect exactly the state of the original models. Thus, a more direct way for achieving XAI is through interpretable (also called glass-box) models. These models have been shown to obtain comparable (and, in some cases, better) performance with respect to black-boxes models in various tasks such as classification and reinforcement learning. However, they struggle when working with raw data, especially when the input dimensionality increases and the raw inputs alone do not give valuable insights on the decision-making process. Here, we propose to use end-to-end pipelines composed of multiple interpretable models co-optimized by means of evolutionary algorithms, that allows us to decompose the decision-making process into two parts: computing high-level features from raw data, and reasoning on the extracted high-level features. We test our approach in reinforcement learning environments from the Atari benchmark, where we obtain comparable results (with respect to black-box approaches) in settings without stochastic frame-skipping, while performance degrades in frame-skipping settings.

In recent years, symbolic regression has been of wide interest to provide an interpretable symbolic representation of potentially large data relationships. Initially circled to genetic algorithms, symbolic regression methods now include a variety of Deep Learning based alternatives. However, these methods still do not generalize well to real-world data, mainly because they hardly include domain knowledge nor consider physical relationships between variables such as known equations and units. Regarding these issues, we propose a Reinforcement-Based Grammar-Guided Symbolic Regression (RBG2-SR) method that constrains the representational space with domain-knowledge using context-free grammar as reinforcement action space. We detail a Partially-Observable Markov Decision Process (POMDP) modeling of the problem and benchmark our approach against state-of-the-art methods. We also analyze the POMDP state definition and propose a physical equation search use case on which we compare our approach to grammar-based and non-grammarbased symbolic regression methods. The experiment results show that our method is competitive against other state-of-the-art methods on the benchmarks and offers the best error-complexity trade-off, highlighting the interest of using a grammar-based method in a real-world scenario.

This complex manufacturing environment is characterized by a large product and batch size variety, numerous parallel machines with large capacity differences, sequence and machine dependent setup times and machine eligibility constraints. A hybrid genetic algorithm is proposed to improve the scheduling process, the main features of which are a local search enhanced crossover mechanism, two additional fast local search procedures and a user-controlled multi-objective fitness function. Testing with real-life production data shows that this multi-objective approach can strike the desired balance between production time, setup time and tardiness, yielding high-quality practically feasible production schedules.

Biologically-inspired AI methods like evolutionary algorithms have shown great promise in creating complex structures yet these structures still pale in comparison to their natural counterparts. The recently introduced generative encoding compositional pattern producing networks (CPPNs), which is based on the principles of how natural organisms develop, narrowed this gap by showing that it is possible to artificially evolve life-like patterns with regularities at a high-level of abstraction. As these generative and developmental systems (GDS) are asked to evolve increasingly complex structures, the question of how to start evolution from a promising part of the search space becomes more and more important. To address this challenge, we introduce the concept of a CPPN-Compiler, which allows the user to directly compile a high-level description of the desired starting structure into the CPPN itself. In this paper, as proof of concept, the CPPN-Compiler is able to generate CPPN-encoded representations from vector-based images that can serve as the starting point for further evolution. Importantly, the offspring of these compiled CPPNs show meaningful variations because they directly embody important domain-specific regularities like symmetry or repetition. Thus the results presented in this paper open up a new research direction in GDS, in which specialized CPPN-Compilers for different domains could help to overcome the black box of evolutionary optimization.

An intelligent agent can display behavior that is not directly related to the task it learns. Depending on the adopted AI framework and task formulation, such behavior is sometimes attributed to environment exploration, or ignored as irrelevant, or even penalized as undesired. We postulate here that virtually every interaction of an agent with its learning environment can result in outcomes that carry information which can be potentially exploited to solve the task. To support this claim, we present Pattern Guided Evolutionary Algorithm (PANGEA), an extension of genetic programming (GP), a genre of evolutionary computation that aims at synthesizing programs that display the desired input-output behavior. PANGEA uses machine learning to search for regularities in intermediate outcomes of program execution (which are ignored in standard GP), more specifically for relationships between these outcomes and the desired program output. The information elicited in this way is used to guide the evolutionary learning process by appropriately adjusting program fitness. An experiment conducted on a suite of benchmarks demonstrates that this architecture makes agent learning more effective than in conventional GP. In the paper, we discuss the possible generalizations and extensions of this architecture and its relationships with other contemporary paradigms like novelty search and deep learning.

Creating a musical fitness function is largely subjective and can be critically affected by the designer's biases. Previous attempts to create such functions for use in genetic algorithms lack scope or are prejudiced to a certain genre of music. They also are limited to producing music strictly in the style determined by the programmer. We show in this paper that musical feature extractors, which avoid the challenges of qualitative judgment, enable creation of a multi-objective function for direct music production. The main result is that the multi-objective fitness function enables creation of music with varying identifiable styles. To demonstrate this, we use three different multi-objective fitness functions to create three distinct sets of musical melodies. We then evaluate the distinctness of these sets using three different approaches: a set of traditional computational clustering metrics; a survey of non-musicians; and analysis by three trained musicians.

The author's GenJam project has spanned nearly 20 years of development and performance. This demonstration provides a brief overview of GenJam's current configuration illustrated with a visually annotated performance of a tune.

The author has been performing with GenJam, the Genetic Jammer, for nearly 20 years and has accumulated a wealth of experiences in performing live jazz with technology. This paper presents a discussion of the use of technology in jazz, both from the performer's and from the audience's perspective, and it proposes a classification scheme for live performance that is geared to mainstream performing situations.

This paper discusses the development of an audio-visual composition based on genetic algorithms strategies. The genetic algorithm's fitness function dynamically adjusts the optimisation targets linked to the mechanisms responsible for the generating of drone soundscapes. The fitness function continuously changes based on the results of an analysis of the visual elements of the artwork thus acting as disturbance factor. In doing so, the audio material never achieves full optimisation and constantly shapes itself. The paper offers both a technical and aesthetic analysis of the development of the composition.

In presence of multiple objectives to be optimized in Search-Based Software Engineering (SBSE), Pareto search has been commonly adopted. It searches for a good approximation of the problem's Pareto optimal solutions, from which the stakeholders choose the most preferred solution according to their preferences. However, when clear preferences of the stakeholders (e.g., a set of weights which reflect relative importance between objectives) are available prior to the search, weighted search is believed to be the first choice since it simplifies the search via converting the original multi-objective problem into a single-objective one and enable the search to focus on what only the stakeholders are interested in. This paper questions such a "weighted search first" belief. We show that the weights can, in fact, be harmful to the search process even in the presence of clear preferences. Specifically, we conduct a large scale empirical study which consists of 38 systems/projects from three representative SBSE problems, together with two types of search budget and nine sets of weights, leading to 604 cases of comparisons. Our key finding is that weighted search reaches a certain level of solution quality by consuming relatively less resources at the early stage of the search; however, Pareto search is at the majority of the time (up to 77% of the cases) significantly better than its weighted counterpart, as long as we allow a sufficient, but not unrealistic search budget. This, together with other findings and actionable suggestions in the paper, allows us to codify pragmatic and comprehensive guidance on choosing weighted and Pareto search for SBSE under the circumstance that clear preferences are available. All code and data can be accessed at: https://github.com/ideas-labo/pareto-vs-weight-for-sbse.