Reviewerly/exHarmony huggingface dataset:
An overview of LCS research from 2021 to 2022

The International Workshop on Learning Classifier Systems (IWLCS) is an annual workshop at the GECCO conference where new concepts and results regarding learning classifier systems (LCSs) are presented and discussed. One recurring part of the workshop agenda is a presentation that reviews and summarizes the advances made in the field over the last year; this is intended to provide an easy entry point to the most recent progress and achievements. The 2020 and 2021 presentations were accompanied by survey workshop papers, a practice which we hereby continue. We give an overview of all the LCS-related publications from 11 March 2021 to 10 March 2022. The 42 publications we review are grouped into six overall topics: Formal theoretic advances, new LCS architectures, LCS-based reinforcement learning, algorithmic improvements to existing LCSs, combinations of LCS and Deep Learning models and, finally, a variety of applications of LCSs.
Coefficient mutation in the gene-pool optimal mixing evolutionary algorithm for symbolic regression

Currently, the genetic programming version of the gene-pool optimal mixing evolutionary algorithm (GP-GOMEA) is among the top-performing algorithms for symbolic regression (SR). A key strength of GP-GOMEA is its way of performing variation, which dynamically adapts to the emergence of patterns in the population. However, GP-GOMEA lacks a mechanism to optimize coefficients. In this paper, we study how fairly simple approaches for optimizing coefficients can be integrated into GP-GOMEA. In particular, we considered two variants of Gaussian coefficient mutation. We performed experiments using different settings on 23 benchmark problems, and used machine learning to estimate what aspects of coefficient mutation matter most. We find that the most important aspect is that the number of coefficient mutation attempts needs to be commensurate with the number of mixing operations that GP-GOMEA performs. We applied GP-GOMEA with the best-performing coefficient mutation approach to the data sets of SR-Bench, a large SR benchmark, for which a ground-truth underlying equation is known. We find that coefficient mutation can help rediscovering the underlying equation by a substantial amount, but only when no noise is added to the target variable. In the presence of noise, GP-GOMEA with coefficient mutation discovers alternative but similarly-accurate equations.
Accelerating genetic algorithm evolution via ant-based mutation and crossover for application to large-scale TSPs

Genetic Algorithms (GAs) have been commonly applied to difficult permutation problems such as the Traveling Salesman Problem (TSP). However, GAs have scalability issues when applied to very large-scale TSPs without resorting to local search. A GA relies on successively improving a population of solutions through crossover and mutation operations. However, these actions are performed blindly relying on natural selection to combine and improve upon good solutions. Alternatively, Ant Colony Optimisation simulates ants which learn the problem structure at a localised level via a pheromone matrix. This paper proposes that a GA could be accelerated by utilising the attributes of ants to construct solutions in a more intelligent manner, in effect hot-wiring GAs. This hypothesis is tested using ant-based mutation and an ant-based edge recombination crossover operator ER-ACO. Measured against art-based TSP instances of up to 200,000 cities both ant-based mutation and crossover improve GA evolution considerably.
Initialisation and grammar design in grammar-guided evolutionary computation

Grammars provide a convenient and powerful mechanism to define the space of possible solutions for a range of problems. While recent work has shed light on the matters of initialisation and grammar design with respect to grammatical evolution (GE), their impact on other methods, such as random search and context-free grammar genetic programming (CFG-GP), is largely unknown. This paper examines GE, random search and CFG-GP on benchmark problems using different initialisation routines and grammar designs. Results suggest that CFG-GP is less sensitive to initialisation and grammar design than both GE and random search: we also demonstrate that observed cases of poor performance by CFG-GP are managed through simple adjustment of tuning parameters. We conclude that CFG-GP is a strong base from which to conduct grammar-guided evolutionary search, and that future work should focus on understanding the parameter space of CFG-GP for better application.
Towards an interactive ranking operator for NSGA-II

Knowing the Decision Makers (DM) preferences during the search process may help the algorithms to find solutions adequate to the DM profile. For this purpose, some interactive approaches allow the DM to evaluate solutions during the search process, rating them with scores. These scores represent the adequacy level of the solutions concerning the DM preferences and should influence the evolution of the search algorithm. Nevertheless, in these interactive approaches, this prioritization is partial and/or specific for a problem domain. In this context, this work aims to propose an initial version of an interactive ranking operator for NSGA-II, whose goal is to support the ranking of solutions considering both the DM preferences and the fitness of the solutions, for any software engineering domain. For validating the proposal, we incorporate this operator in an interactive approach for Product Line Architecture design. The results pointed out that the new ranking operator can properly deal with the DM preferences, giving a greater chance of surviving to those solutions with higher scores without compromising the diversity of solutions.
Comparing different metaheuristics for model selection in a supervised learning classifier system

In the context of tasks that highly involve human interaction and expert knowledge, i.e., operator guidance in manufacturing, the possibility of decision verifications by the user is a key requirement for inspiring confidence in a system and its predictions. Rule-based Machine Learning offers one way to create such systems, with Learning Classifier Systems being a family of algorithms whose models are by design human-interpretable. Obtaining a rule base as compact and accurate as possible is a mandatory prerequisite to increasing comprehensibility, and metaheuristics such as Genetic Algorithms or Particle Swarm Optimization are powerful approaches to reducing the size of large rule bases. In particular, this paper will analyze five population-based metaheuristics and their ability to compose solutions (rule subsets) as part of a newly developed rule-based learning system, the Supervised Rule-based Learning System (SupRB). The experiments suggest that all metaheuristics can significantly reduce the complexity and filter out obstructive rules, increasing the prediction quality in the process.
NL-SOMA-CLP for real parameter single objective bound constrained optimization

This paper presents a competition entry for the Real Parameter Single Objective Bound Constrained Optimization at The Genetic Evolutionary Computation Conference (GECCO) 2022. A new variant of Self-organizing Migrating Algorithm with CLustering-aided migration and adaptive perturbation vector control (SOMA-CLP) is proposed in this paper, which is titled Non-Linear Population Size Reduction and Nearest Neighbor Leader Selection SOMA-CLP(NL-SOMA-CLP). Overall, NL-SOMA-CLP improves the SOMA-CLP by using two techniques: a) a non-linear automatic reduction of population size and b) a nearest neighbor leader selection strategy.
MFENAS

Neural Architecture Search (NAS) aims to automatically find neural network architectures competitive with human-designed ones. Despite the remarkable progress achieved, existing NAS methods still suffer from vast computational resources cost. Inspired by MFEA, we model the NAS task as a two-factorial problem and propose a multifactorial evolutionary neural architecture search (MFENAS) algorithm to solve it. MFENAS divides a population into two subgroups according to factors, and then the factors influence the evolution and knowledge transfer between subgroups. Experimental results of NATS-Bench demonstrate the efficiency of the proposed MFENAS in finding optimal structures under resource constraints compared to other state-of-the-art methods.
GUI-based, efficient genetic programming for Unity3D

Unity3D is a game development environment that could be co-opted for agent-based machine learning research. We present a GUI-driven, and efficient Genetic Programming (GP) system for this purpose. Our system, ABL-Unity3D, addresses challenges entailed in co-opting Unity3D: making the simulator serve agent learning rather than humans playing a game, lowering fitness evaluation time to make learning computationally feasible, and interfacing GP with an AI Planner to support hybrid algorithms that could improve performance. We achieve this through development of a GUI using the Unity3D editor's programmable interface, and performance optimizations. These optimizations result in at least a 3x speed up. We describe ABL-Unity3D by explaining how to use it for an example experiment using GP and AI Planning.
Benchmarking CMA-ES with margin on the bbob-mixint testbed

The CMA-ES with Margin (CMA-ESwM) is a CMA-ES variant recently proposed for mixed-integer black-box optimization (MI-BBO), which introduces a lower bound on the marginal probability associated with integer variables. The CMA-ESwM shows promising performance compared to existing methods on simple benchmark functions. However, its performance has not been comprehensively investigated in other function classes, such as multimodal ones. In this work, we investigate the performance of the CMA-ESwM on the bbob-mixint testbed that includes problems of various properties for MI-BBO. The experimental results show that the CMA-ESwM outperforms the other MI-BBO methods at higher dimensions. The performance at low dimensions is competitive with the comparative methods.
Safety-informed mutations for evolutionary deep reinforcement learning

Evolutionary Algorithms have been combined with Deep Reinforcement Learning (DRL) to address the limitations of the two approaches while leveraging their benefits. In this paper, we discuss objective-informed mutations to bias the evolutionary population toward exploring the desired objective. We focus on Safe DRL domains to show how these mutations exploit visited unsafe states to search for safer actions. Empirical evidence on a 12 degrees of freedom locomotion benchmark and a practical navigation task, confirm that we improve the safety of the policy while maintaining comparable return with the original DRL algorithm.
A comparative analysis on genome pleiotropy for evolved soft robots

Biological evolution shapes the body and brain of living creatures together over time. By contrast, in evolutionary robotics, the co-optimization of these subsystems remains challenging. Conflicting mutations cause dissociation between morphology and control, which leads to premature convergence. Recent works have proposed algorithmic modifications to mitigate the impact of conflicting mutations. However, the importance of genetic design remains underexposed. Current approaches are divided between a single, pleiotropic genetic encoding and two isolated encodings representing morphology and control. This design choice is commonly made ad hoc, causing a lack of consistency for practitioners. To standardize this design, we performed a comparative analysis between these two configurations on a soft robot locomotion task. Additionally, we incorporated two currently unexplored alternatives that drive these configurations to their logical extremes. Our results demonstrate that pleiotropic representations yield superior performance in fitness and robustness towards premature convergence. Moreover, we showcase the importance of shared structure in the pleiotropic representation of robot morphology and control to achieve this performance gain. These findings provide valuable insights into genetic encoding design, which supply practitioners with a theoretical foundation to pursue efficient brain-body co-optimization.
Exploring expression-based generative adversarial networks

Before the advent of Generative Adversarial Networks (GANs) in 2014, Evolutionary Computation approaches made up the majority of the state of art for image generation. Such approaches have been replaced with adversarial models using deep convolutional networks specifically tailored for GPU computing. Nevertheless, motivated by recent successes in GPU-accelerated Genetic Programming (GP) and given the well-established disposition of expression-based solutions towards image evolution, the prospect of bridging the gap between using symbolic expressions as generators for GANs, instead of neural networks, seems enticing.
COIL: Constrained optimization in learned latent space

Constrained optimization problems can be difficult because their search spaces have properties not conducive to search, e.g., multimodality, discontinuities, or deception. To address such difficulties, considerable research has been performed on creating novel evolutionary algorithms or specialized genetic operators. However, if the representation that defined the search space could be altered such that it only permitted valid solutions that satisfied the constraints, the task of finding the optimal would be made more feasible without any need for specialized optimization algorithms. We propose Constrained Optimization in Latent Space (COIL), which uses a VAE to generate a learned latent representation from a dataset comprising samples from the valid region of the search space according to a constraint, thus enabling the optimizer to find the objective in the new space defined by the learned representation. Preliminary experiments show promise: compared to an identical GA using a standard representation that cannot meet the constraints or find fit solutions, COIL with its learned latent representation can perfectly satisfy different types of constraints while finding high-fitness solutions.
Interaction-transformation evolutionary algorithm with coefficients optimization

Symbolic Regression is the task of finding a mathematical expression to describe the relationship between one or more independent variables with a dependent variable. The search space can be vast and include any algebraic function; thus, finding optimal values for coefficients may not be a trivial task. The Interaction-Transformation representation alleviates this problem enforcing that the coefficients of the expression is part of a linear transformation, allowing the application of least squares. But this solution also limits the search space of the expressions. This paper proposes four different strategies to optimize the coefficients of the nonlinear part of the Interaction-Transformation representation. We benchmark the proposed strategies by applying the Interaction-Transformation Evolutionary Algorithm (ITEA) to six well-known data sets to evaluate four optimization heuristics combining linear and non-linear methods. The results show that optimizing the non-linear and linear coefficients separately was the best strategy to find better-performing expressions with a higher run-time and expression size. The non-linear optimization method alone was the worst-performing method.
Comparative analysis of optimization strategies by software complex “Metaheuristic nature-inspired methods of global optimization”

Abstract This article discusses the comparative efficiency analysis results of algorithms for finding a global extrema for multivariable functions with parallelepiped constraints. Three metaheuristic nature-inspired optimization algorithms such as Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA) and Perch School Search (PSS) are presented. For algorithms comparative analysis the software complex was created, which allows visualizing a process of obtaining the approximate solution of standard generally accepted benchmark problems. The different examples of optimization algorithms results are given. Benchmark functions structure provides fairly estimating of compared optimization methods. The purpose of the paper is collecting and subsequent analysis of statistical results of algorithms accuracy and convergence pattern. Statistical data analysis makes it possible to choose the most suitable optimization method and to formulate valid recommendations to pick parameters for the most efficient solution of arbitrary objective function optimization problem.
SeSAME: Re-identification-based ambient intelligence system for museum environment

Nowadays, understanding and analysing visitors activities and behaviours is becoming imperative for personalising and improving the user experience in a museum environment. Users’ behaviour can provide important statistics, insights and objective information about their interactions, such as attraction, attention and action. These data represent a precious value for the museum curators, and they are one of the parameters that need to be assessed. These information are collected through manual approaches based on questionnaires or visual observations. This procedure is time consuming and can be affected by the subjective interpretation of the evaluator. From such premises, SeSAME (Senseable Self Adapting Museum Environment) a novel system for collecting and analysing the behaviours of visitors inside a museum environment is presented in this paper. SeSAME is based on a multi-modal deep neural network architecture able to extract anthropometric and appearance features from RGB-D videos acquired in crowded environments. Our approach has been tested on four different temporal modelling methods to aggregate a sequence of image-level features into clip-level features. This paper uses as a benchmark TVPR2, a public dataset of acquired videos with an RGB-D camera in a top-view configuration, in the presence of persistent and temporarily heavy occlusion. Moreover, a dataset specifically collected for this work has been acquired in a real museum environment, which is Palazzo Buonaccorsi, an important historical building in Macerata, in Marche Region in the center of Italy. During the experimental phase, the evaluation metrics show the effectiveness and the suitability of the proposed method.
Inferring causal networks of health care resilience and safety performance indicators: A two-stage fuzzy cognitive map approach

In the literature, there are three major concepts used to model resilience and safety performance: (i) integrated resilience engineering (IRE), (ii) work motivational factors, (iii) health, safety, environment, and ergonomics (HSEE). Over the last few decades, health care has developed a compartmentalized vision of performance, safety, and resilience. The issues caused by this are not yet clearly understood since the developments in each dimension are in their infancy. This paper presents a two-stage fuzzy cognitive map (FCM) using a non-linear Hebbian learning (NHL) algorithm and three evolutionary algorithms (EAs) to model the causal relations among these concepts and their impact on health care performance. This paper aims to infer the causal network of these concepts. We use an FCM trained in two stages to show the synergistic relationships among the health and safety paradigms and their favorable effects on the organization's safety performance. The methodology developed in this study targets achieving the minimum learning error by running an NHL algorithm followed by three EAs comprising genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competitive algorithm (ICA). The method is applied to a case study of a large general hospital. While a mere defuzzification of the experts' judgments yields virtually no distinguishability between the individual factors and their impact on performance, our approach extracts a much-improved degree of differentiation. One striking example is the obtained high impact of self-organization and overall workload on resilience and safety performance.
Unsupervised manifold learning with polynomial mapping on symmetric positive definite matrices

In this paper, an unsupervised manifold learning algorithm with polynomial mapping on the symmetric positive-definite (SPD) matrix manifold is introduced by matrix information geometry method for data dimensional reduction. Firstly, the mathematical knowledge about the SPD matrix manifold is presented including the metric, geodesic and submanifold. And then, the high dimensional information coordinates are given by different SPD matrix data for constructing the polynomial kernel matrix, weight matrix and sparsity preserving matrix. Next, the manifold learning algorithm on the SPD matrix manifold is proposed by polynomial mapping with geodesic distance. Finally, comparing with some conventional methods in terms of accuracy rate and time cost, the preliminary analysis results indicate that the proposed approach is able to offer a consistent and comprehensive method to realize the SPD matrix data dimensional reduction.
A methodology for task-specific metrological characterization of low-cost 3D camera for face analysis

Facial appearance is one prominent feature in analyzing several aspects, e.g., aesthetics and expression of emotions, and face analysis is crucial in many fields. Face analysis requires measurements that can be performed by different technologies and typically relies on landmarks identification. Recently, low-cost customer grade 3D cameras have been introduced in the market, enabling an increase of application at affordable cost with nominal adequate performances. Novel cameras require to be thoroughly metrologically characterized to guarantee these performances. Cameras are calibrated following a standard general-purpose procedure. However, the specificity of facial measurements requires a task-based metrological characterization to include typical influence factors. This work outlines a methodology for task-based metrological characterization of low-cost 3D cameras for facial analysis, consisting of: influence factor identification by ANOVA, related uncertainty contribution assessment, uncertainty propagation, landmarking uncertainty estimation. The proposed methodology is then demonstrated on a customer grade state-of-the-art 3D camera available on the market.
A security reference architecture for cargo ports

Secure systems must be built in a systematic and holistic way, where security is an integral part of the development lifecycle and cuts across all architectural layers. This need is more evident in Cyber Physical Systems (CPSs), where attacks may target not only the information model of the system but also its physical entities. CPS systems are heterogeneous and often highly complex. Their possibly numerous components and cross-domain complexity make attacks easy to propagate and security difficult to implement. Moreover, this complexity results in a considerable variety of vulnerabilities and a large attack surface. To design secure CPS systems a good approach is to abstract their complexity and develop a common framework, namely a Reference Architecture (RA), to which we add security mechanisms in appropriate places to stop its threats to define a Security Reference Architecture (SRA). An SRA is an abstract architecture describing a conceptual model of security that provides a way to specify security requirements for a wide range of derived concrete architectures. An important type of CPS is a maritime container terminal, a facility where cargo containers are transported between ships and land vehicles for onward transportation, and vice versa. We present here an SRA for cargo ports built out of patterns, which goes beyond existing models in providing a global view and a more precise description than just block diagrams. Starting from an RA, we analyze security issues in each activity of the processes of the system and enumerate its threats. We describe threats using misuse patterns, and from them we select security patterns that realize defensive solutions.