Reviewerly/exHarmony huggingface dataset:
Interactive GA Approach for Video Digest Suggestion

This paper presents an interactive genetic algorithm (IGA) approach for video digest suggestion. The goal of the approach is to personalize video digest, which is personalized summary of video, by using a method with less fatigue. This approach utilizes the process called Preferred Diversity-based Interactive Genetic Algorithm (PD-IGA), which incorporates the preference shot method to swiftly consider user preferences in the optimization process. The use of diversity preserving sampling is also included to prevent early convergence. In order to validate the performance of the PD-IGA process in the proposed approach, a simulation experiment using human model was performed on video data of varying lengths. The results of these experiments showed that the proposed method generates higher quality video digests compared to traditional IGA algorithms. The personalized video digest system using this approach was able to produce high-quality video digest according to the user's personalized purpose.
From Fitness Landscapes to Explainable AI and Back

We consider and discuss the ways in which search landscapes might contribute to the future of explainable artificial intelligence (XAI), and vice versa. Landscapes are typically used to gain insight into algorithm search dynamics on optimisation problems; as such, it could be said that they explain algorithms and that they are a natural bridge between XAI and evolutionary computation. Despite this, there is very little existing literature which utilises landscapes for XAI, or which applies XAI techniques to landscape analysis. This position paper reviews the existing works, discusses possible future avenues, and advocates for increased research effort in this area.
Toward Symbolic Regression based Model Transform for Convolutional Neural Network

This paper introduces a symbolic regression based filter transform for convolutional neural network using CGP (Cartesian Genetic Programming). Symbolic regression is a powerful technique to discover analytic equations that describe data, which can lead to explainable models and the ability to predict unseen data. In contrast, neural networks have achieved amazing levels of accuracy on image recognition and natural language processing tasks, but they are often seen as black-box models that are difficult to interpret and typically extrapolate poorly. symbolic regression approaches to deep learning are underexplored.
GECCO'2022 Symbolic Regression Competition: Post-Analysis of the Operon Framework

Operon is a C++ framework for symbolic regression with the ability to perform local search by optimizing model coefficients using the Levenberg-Marquardt algorithm. This enhancement has proven to be effective in a variety of regression tasks. Operon took part in the Interpretable Symbolic Regression for Data Science hosted at the 2022 Genetic and Evolutionary Computation Conference, where it ranked overall 4th based on criteria of accuracy, simplicity as well as task-specific goals. Although accurate, the returned models were exceedingly complex and ranked poorly in terms of simplicity. In this paper, we investigate the application of the Minimum Description Length (MDL) principle for selecting models with a better compromise between accuracy and complexity from the final Pareto front returned by the algorithm. A new experiment on the synthetic track of the competition highlights the critical role played by model selection in algorithm performance. The MDL-enhanced approach obtains the best overall score and demonstrates excellent results on all synthetic tracks.
Leap Mapping: Improving Grammatical Evolution for Modularity Problems

We introduce Leap mapping, a new mapping process for Grammatical Evolution (GE), which spreads introns within the effective length of the genome (the part of the genome consumed while mapping), preserving information for future generations and performing less disruptive crossover and mutation operations than standard GE. Using the exact same genotypic representation as GE, Leap mapping reads the genome in separate parts named 'frames', where the size of each is the number of production rules in the grammar. Each codon inside a frame is responsible for mapping a different production rule of the grammar. The process keeps consuming codons from the frame until it needs to map again a production rule already mapped with that frame. At this point, the mapping starts consuming codons from the next frame. We assessed the performance of this new mapping in some benchmark problems, which require modular solutions: four Boolean problems and three versions of the Lawnmower problem. Moreover, we compared the results with the standard mapping procedure and a multi-genome version.
Evolutionary Algorithms: A new hope for the future of music teaching

In this paper, we describe the experience gained when Evolutionary Algorithms were applied to SATB music composition and the impact it is demonstrating in Spanish Professional Music Conservatories for the last three years. To our knowledge, this is the first time that an EA-inspired tool has been used at a national level in music conservatories.
Hierarchical Frames-of-References in Learning Classifier Systems

Perceptual aliasing is one of the most important problems in Robot-navigation as a robot cannot distinguish its state via its immediate observations leading to poor decision-making. Frames-of-References-based XCS learns policies comprising of constituent-level paths (that may be aliased) integrated into a holistic-level path that has unique patterns of the environment leading to improved policy performance. However, unique references are required to identify an aliased state such that is impractical to learn a policy in an environment with multiple, sequential, aliased states, e.g., a long, uniform, corridor. This paper introduces methods for hierarchical references, which concatenate sequential aliased states to form a discrete unique state and references them using the end-of-the-series state. The experiments investigated the performance of the proposed system in partially observable environments with complex aliasing patterns, including sequential aliased states. The results showed that the proposed system overcomes the state-of-the-art system's issues in the tested problems; learning in sequential aliased states, unlike the previous system.
Residual encoding framework to compress DNN parameters for fast transfer

Efficient communication is significant for federated learning and DNN model deployment. However, transferring hundreds of millions of DNN parameters over networks with limited bandwidth results in long communication delays or even data losses. To alleviate or even remove the communication bottleneck, efficient methods for parameter compression can be applied. Inspired by video encoding, which exploits inter-frame similarity for compression, we investigate the strong temporal correlations of parameter updates in two near epochs of the DNN model and introduce a model parameter residual encoding framework. By transmitting encoded residual between model parameters in two near epochs, the receiver can reconstruct new model parameters and finish the updates with less communication cost. Furthermore, with respect to our framework, we develop lossless and lossy model parameter compression methods and demonstrate them on popular classification and detection networks. The results show that the lossless method can compress the data size of the parameters to less than 90%, and the lossy method can shrink the parameter size to less than 50% with a fair low loss. Our source code is released at https://github.com/zhouliguo/DNN_param_encode.
The Effect of Applying Blended Learning Using Interactive Writing on Learning Interest and Indonesian Writing Skills in Writing

This study deals with the effect of blended learning using interactive writing on learning to write simple Indonesian essays on interests and writing skills. This study aims to determine the influence of the application of good learning methods by teachers and students after their application in the learning process. This research method uses a quantitative research design Quasi-Experimental Design with the Nonquivalent Control Group Design type. The population in this study were all 123 class III students. The subjects in this study were students of class III D at SD Telkom Makassar. The data collection technique was filling out a student response questionnaire and written test. The data analysis technique used is quantitative data analysis techniques. The results of the analysis of the description of the interest of the experimental class (III D) students obtained by the researcher showed an increase in student interest after the application of blended learning using interactive writing in a very good category, while for the control class (III B) the results of the analysis of the description of student interest showed an increase but still in the poor category. The results of the description of the writing skills of the experimental class students showed a significant increase after the application of blended learning using interactive writing. There was no significant difference between the students' writing skills in the control class at the pre-test and post-test. The results showed that it was based on multivariate and manova tests. So the application of blended learning using interactive writing affects students' interest and writing skills.
Quality Diversity optimization using the IsoLineDD operator: forward and backward directions are equally important

Quality Diversity (QD) optimization aims at returning a diverse collection of high quality solutions in a single run. Prior work indicated that the optimized collection concentrates in a subspace of the genotype space called the Elite Hypervolume. This suggested that if the Elite Hypervolume is convex, perturbing a solution (elite) towards the direction of another elite would create a solution inside the subspace. To accelerate QD optimization, the IsoLineDD operator was proposed which perturbs a solution along the line that connects it with another elite, both in the forward and the backward direction (i.e., towards and away from another elite), with equal probability. In this work, we hypothesize that the backward direction is mostly useful for exploration, at the beginning of QD optimization, rather than exploitation. To validate our hypothesis, we create a dynamic IsoLineDD operator that modifies its probability of creating solutions in the forward and backward directions, respectively. Our experiments and analysis in the robotic arm repertoire and Rastrigin problems invalidate this hypothesis by demonstrating that the forward and backward directions of vanilla IsoLineDD equally contribute to the optimization of the collection.
Development of forked divergence particle swarm optimization

There are a lot of types of PSO to make conventional PSO as effective as possible. Basically, PSO is quite easy, and it is known as effective in any type of problems within several numbers of design variables. However, requirements of industrial problems become complicated that the number of design variables raise these days, and the following shortcoming make conventional PSO difficult to apply to such problems. The performance of conventional PSO depends on setting three parameters, and most of them set them priori to execution. This makes it impossible to search according to the situation of convergence. Once velocities become close to zero, those individuals lost ways to get out from local minimum. Therefore, we need some procedure like mutation in Genetic Algorithms. In large scale problems, there are some sort of dependencies in design variables. The effectiveness of PSO does not lose if the variable relationships and sets of relatively small numbers of design variables in problems are found. In this paper, we propose a new method Forked Divergence Particle Swarm Optimization (PSO) in order to overcome problems in conventional PSO. In the experiment, the effectiveness of the proposed method is validated through two types of benchmark functions.
Re-Evaluating Algorithm Variations Using Empirical Similarity

In the context of metaheuristic search algorithms, two recent approaches have been proposed to measure algorithm similarity. The first one is based on shared search strategies, such as mutation or selection. The second one involves an empirical analysis that uses a set of performance metrics on benchmark problems. This paper explores whether high Component Similarity corresponds to Performance Similarity by analyzing these two similarity metrics on 9 CMA-ES variants on the COCO benchmark.
Controlling Hybrid Evolutionary Algorithms in Subset Selection for Multimodal Optimization

Multimodal functions play a central role in artificial intelligence and evolutionary algorithms. Still, there are several limitations when it comes to existing work on optimization of complex multimodal functions. In this paper, we study the optimization of such functions, in the subset selection problem setting, by carefully integrating different methods: evolutionary algorithms, stochastic local search, clustering, and feedback control. The goal is to carefully balance exploration and exploitation during hybrid evolutionary search by using feedback control to adapt population diversity via crowding. We empirically test our integrated method on complex synthetic combinatorial optimization problems, demonstrating promising results compared to previous work.
Exploring the Potential of JuLeS: A White Box Framework for Local Search Metaheuristics

The JuLeS framework (short for Julia Local Search)1 is a versatile and highly customizable platform for quickly creating Local Search solvers. Developed using the Julia programming language, JuLeS enables efficient implementation of solvers, facilitates the development of new meta-heuristic algorithms, and easily integrates with existing tools. The overhead of the framework with respect to existing C++ frameworks is moderate, but it benefits from a lower programming effort and integrability into the Julia ecosystem. The design, architecture, and features of JuLeS are explained and its effectiveness is demonstrated through three use cases that highlight its design achievements.
From Random Forest to an interpretable decision tree - An evolutionary approach

Random Forest (RF) is one of the most popular and effective machine learning algorithms. It is known for its superior predictive performance, versatility, and stability, among other things. However, an ensemble of decision trees (DTs) represents a black-box classifier. On the other hand, interpretability and explainability are ones of the top artificial intelligence trends, to make predictors more trustworthy and reliable. In this paper, we propose an evolutionary algorithm to extract a single DT that mimics the original RF model in terms of predictive power. The initial population is composed of trees from RF. During evolution, the genetic operators modify individuals (DTs) and exploit the initial (genetic) material. e.g., splits/tests in the tree nodes or more expanded parts of the DTs. The results show that the classification accuracy of a single DT predictor is not worse than that of the original RF. At the same time, and probably most importantly, the resulting classifier is a single smaller-size DT that is almost self-explainable.
Performance Analysis of Self-Supervised Strategies for Standard Genetic Programming

Self-supervised learning (SSL) methods have been widely used to train deep learning models for computer vision and natural language processing domains. They leverage large amounts of unlabeled data to help pretrain models by learning patterns implicit in the data. Recently, new SSL techniques for tabular data have been developed, using new pretext tasks that typically aim to reconstruct a corrupted input sample and yielding models which are, ideally, robust feature transforms. In this paper, we pose the research question of whether genetic programming is capable of leveraging data processed using SSL methods to improve its performance. We test this hypothesis by assuming different amounts of labeled data on seven different datasets (five OpenML benchmarking datasets and two real-world datasets). The obtained results show that in almost all problems, standard genetic programming is not able to capitalize on the learned representations, producing results equal to or worse than using the labeled partitions.
Optimized hybrid imbalanced data sampling for decision tree training

For many real-world decision-making tasks, a key feature is decision explainability. Hence, the so-called glass-box models offer full explainability and are still prevalent. An important area of application is the classification of imbalanced data. We require that the proposed classifiers not make errors on the minority class while minimizing errors on the majority class. This paper proposes a method for preprocessing imbalanced data by generating minority class objects. We use a multi-criteria optimization method (NSGA-II) to avoid optimizing a single aggregate criterion. The method returns a group of non-dominated solutions from which the end user can choose the best solution from his point of view. The automatic solution selection from a Pareto front is also proposed for comparison purposes. The proposed method returns good-quality classifiers, often surpassing the quality of baseline single-objective methods, and is additionally characterized by full interpretability.
Predict, pretrained, select and answer: Interpretable and scalable complex question answering over knowledge bases

Complex question answering (CQA) over knowledge bases(KB) is a challenging task that has attracted increasing attention in recent years. Semantic parsing-based methods face challenges such as poor adaptability for incomplete KB, large search spaces, and high costs to label logic forms. The gap between knowledge graph representations and question token embeddings leads to poor generalizability and uninterpretable reasoning of information retrieval-based methods. We propose an interpretable and scalable system called Predict, Pretrained, Select and Answer (PPSA) to solve CQA tasks over KB. Our system first trains a language model to predict the reasoning paths required to answer questions. We select only the entities that the predicted reasoning paths pass through in the knowledge graph as candidate entities to reduce the amount of distracting information. The paths that connect the topic entity and the selected candidate entity along with the question are then fed into another language model for answer prediction. The answer prediction module loads the parameters of the trained path prediction module before training to improve accuracy. The system reduces the search space by predicting the path and does not need expensive logic forms annotation. The textual path is the input to the language model, which bridges the gap between the graph representations and token embeddings. We analyse the system reasoning ability over knowledge graphs with different degrees of sparseness, and evaluate the system generalizability. The results of experiments performed with the Complex WebQuestions and WebQuestionsSP datasets demonstrate the effectiveness of our approach for CQA task.
Phased Genetic Programming for Application to the Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is a difficult permutation-based optimisation problem typically solved using heuristics or meta-heuristics which search the solution problem space. An alternative is to find sets of manipulations to a solution which lead to optimality. Hyper-heuristics search this space applying heuristics sequentially, similar to a program. Genetic Programming (GP) evolves programs typically for classification or regression problems. This paper hypothesizes that GP can be used to evolve heuristic programs to directly solve the TSP. However, evolving a full program to solve the TSP is likely difficult due to required length and complexity. Consequently, a phased GP method is proposed whereby after a phase of generations the best program is saved and executed. The subsequent generation phase restarts operating on this saved program output. A full program is evolved piecemeal. Experiments demonstrate that whilst pure GP cannot solve TSP instances when using simple operators, Phased-GP can obtain solutions within 4% of optimal for TSPs of several hundred cities. Moreover, Phased-GP operates up to nine times faster than pure GP.
Exploring Adaptive Components of SOMA

This research paper aims to explore the possibilities of parameterization of state-of-the-art adaptive mechanisms incorporated in the self-organizing migrating algorithm (SOMA). This algorithm has gained renewed interest from the research community while the algorithm's internal dynamics, mechanisms, and dependencies of the functionality on parameter settings have not been thoroughly examined using a data-driven approach. Our extensive test workflow has yielded valuable insights into the performance of the SOMA and its sensitivity to parameter settings that affect the migration of individuals. Important findings were also obtained regarding the appropriate parameter settings required for more complex techniques such as clustering, organization, and population restart. The research also highlighted the influence of different modern adaptation techniques and the suitability of combining different modern adaptation mechanisms, leading to the modular design of different configurations. The simulation experiments conducted provided new insights that could be useful for further research, especially with the rapid development in automatic configurators of meta-heuristic algorithms, modular concepts of algorithms, and their performance prediction.
Controllable Exploration of a Design Space via Interactive Quality Diversity

This paper introduces a user-driven evolutionary algorithm based on Quality Diversity (QD) search. During a design session, the user iteratively selects among presented alternatives and their selections affect the upcoming results. We implement a variation of the MAP-Elites algorithm where the presented alternatives are sampled from a small region (window) of the behavioral space. After a user selection, the window is centered on the selected individual's behavior characterization, evolution selects parents from within this window to produce offspring, and new alternatives are sampled. Essentially we define an adaptive system of local QD search, where the user's selections guide the search towards specific regions of the behavioral space. The system is tested on the generation of architectural layouts, a constrained optimization task, leveraging QD search through a two-archive approach.
Modularity Based Linkage Model For Neuroevolution

Crossover between neural networks is considered disruptive due to the strong functional dependency between connection weights. We propose a modularity-based linkage model at the weight level to preserve functionally dependent communities (building blocks) in neural networks during mixing. A proximity matrix is built by estimating the dependency between weights, then a community detection algorithm maximizing modularity is run on the graph described by such matrix. The resulting communities/groups of parameters are considered to be mutually independent and used as crossover masks in an optimal mixing EA. A variant is tested with an operator that neutralizes the permutation problem of neural networks to a degree. Experiments were performed on 8 and 10-bit parity problems as the intrinsic hierarchical nature of the dependencies in these problems are challenging to learn. The results show that our algorithm finds better, more functionally dependent linkage which leads to more successful crossover and better performance.