Reviewerly/exHarmony huggingface dataset:
A Preliminary Study of Multi-task MAP-Elites with Knowledge Transfer for Robotic Arm Design

The structure design of robotic arms is of great importance on completing industrial tasks successfully. This is a typical multi-task optimization problem when considering different constraints as different tasks. However, mainstream methods for multi-task optimization such as evolutionary multitasking and Multi-task MAP-Elites algorithms tend to encounter problems such as high computational cost and slow convergence when solving large-scale robotic arm tasks. To this end, this paper proposes a new framework based on the MAP-Elites algorithms for solving large-scale robot arm design tasks, called Multi-task MAP-Elites with Knowledge Transfer (MMKT). Specifically, this paper designs the group-based knowledge transfer process for large-scale task optimization in which all tasks are classified into different groups according to their similarity to generate multiple knowledge transfer areas; and knowledge transfer strategies are designed to enhance the quality of solutions with low fitness value. We test the effectiveness of the MMKT framework in planar robotic arm experiments (2000, 5000, and 10,000 tasks; 10, 15-dimensional search space). The experimental results prove that the MMKT outperforms the MME, CMA-ES, and classical ES algorithms.
Evolutionary Real-world Item Stock Allocation for Japanese Electric Commerce

This work addresses a real-world item stock allocation using evolutionary optimization for Japanese electric-commerce. We use the actual data of items to be ordered, existing warehouses, and order records from customers. The target area is all over Japan. The task is to find the optimal distribution of one thousand items to eight warehouses. The problem has two objectives: minimizing the total shipping cost and minimizing the average number of stocked warehouses. The problem also has constraints, including the warehouse capacities and the maximum possible number of shipping from each warehouse. Since the commonly used uniform crossover tends to be destructive in this problem, we propose four crossovers for the problem: the item, the warehouse, the item uniform, the warehouse uniform crossovers. Experimental results show that the proposed item crossover is suited to solve this problem, and the obtained item stock allocations can significantly reduce shipping and stocking costs compared with a human-made allocation.
Improving the optimization performance by an adaptable design: A dynamic selection of operators via criteria-based matrix for evolutionary algorithms

The balance between exploration and exploitation is an important feature in Evolutionary Algorithms (EA). The use of different operators permits to explore the search space and exploit the most prominent regions. This article introduces a dynamic operator selection method that considers different criteria at the same time. The proposed approach uses a dynamic decision matrix (DyDM) to identify which operators must be used at each iteration based on how the algorithm behaves. The DyDM considers specific information as the diversity of the algorithm to avoid stagnation, the actual iteration to work accordingly, and the fitness to direct the search. The proposed approach is called Dynamic Decision Matrix Optimizer (DyDMO) and it has been compared with different well-known algorithms tested on the CEC 2017 benchmark functions. The comparative analysis and non-parametric statistical tests validate how DyDMO im-proves the quality of the solutions and is more stable than its comnetitors.
Setup of a New Adaptive Fuzzy Particle Swarm Optimization Algorithm

Heuristic optimization methods such as Particle Swarm Optimization (PSO) depend on their parameters to achieve good performance on a given class of problems. Some modifications of heuristic algorithms aim to adapt those parameters during the optimization process. We present a framework to design such adaptation strategies using continuous fuzzy feedback control. Our framework, which is not tied to a particular algorithm, provides us with a simple interface where probes are sampled in the optimization process and parameters are fed back. The process of turning probes into parameters uses fuzzy logic rule sets, where the design of rules aims to maximize performance on a training benchmark. This meta-optimization is achieved by a Bayesian Optimizer (BO) with a Gradient Boosted Regression Trees (GBRT) prior. The robustness of the control is also assessed on a validation benchmark.
Learning the Impact of Group Structure on Optimal Herd Path Planning with Cultural Algorithms

This paper compares several approaches to cooperative multi-agent path planning (MAP) based upon variations of the A* algorithm. To simulate multi-agent migration patterns three path-finding mechanisms based on the classic A* algorithm was utilized: A*; A*, Ambush and Dendriform. Each makes different assumptions about group leadership in terms of their path generation. A* assumes a single leader for the migratory group: A*mbush allows the group to move in waves; and Dendriform allows the group to decompose and recompose into groups of arbitrary size with local leaders. Each mechanism required parameter weightings so that the simulated agents would interact realistically with their environment. Cultural Algorithms were employed to adjust the parameter weight categories in order to optimize the group movement under each of these leadership strategies. The three approaches were applied to the simulation of a real-world multi-agent system, the migration of large herd of caribou. The simulated migration was part of the Deepdive Virtual Reality system. In those simulations A* with a single planning agent emphasized nutrition at the expense of the other parameters. A*mbush learned to reduce nutrition slightly and while increasing its emphasis on risk and exploration. On the other hand, Dendriform emphasized overall effort since its planning more dynamic and required more concentration on local effort to be optimized.
An Adaptive Differential Evolution with Mutation Strategy Pools for Global Optimization

This paper proposes an adaptive differential evolution algorithm with mutation strategy pools for global optimization. In the proposed method, the mutation strategy pool mechanism is devised to supply appropriate mutation strategy for different individuals in the population. Further, a mutation strategy, called DE/current-to-wb/l, has also been designed and employed in the mutation strategy pool. The performance of the proposed algorithm has been evaluated on CEC'2014 benchmark functions and compared with related methods. Experimental results show that the proposed algorithm has a good performance and outperforms related algorithms.
Smart Operators for Inducing Colorectal Cancer Classification Trees with PonyGE2 Grammatical Evolution Python Package

Colorectal cancer is a disease that affects many people and requires a multidisciplinary approach, involving significant human and economic resources. We have been provided with a tabular dataset with 1.5 thousand cases of this disease. We are interested in producing interpretable classifiers for predicting the occurrence of complications. Grammatical Evolution has extensively been used for machine learning problems. In particular, it can be used to induce interpretable decision trees, with the advantage of allowing the practitioner to easily control the language by means of the grammar. PonyGE2 [1], [2] is a Python package that provides data scientists with Grammatical Evolution algorithms, which can be configured to their needs quite easily. In addition, and thanks to the benefits of the Python programming language, PonyGE2 is currently becoming more and more popular. However, the capabilities of PonyGE2 for inducing classification trees are still subject of improvement. In particular, it only uses simple equality conditions and requires to encode feature names and values with numbers. We have developed some smart operators for PonyGE2, which, not only enhance the framework in interpretability and performance when dealing with our colorectal cancer dataset, but also allows to produce results comparable to those of the widely known heuristic methods C4.5 and CART. We show how they could be applied to other datasets, and how they affect performance in our case.
A Study on Six Memetic Strategies for Multimodal Optimisation by Differential Evolution

This paper presents an experimental study on memetic strategies to enhance the performance of population-based metaheuristics for multimodal optimisation. The purpose of this work is to devise some recommendations about algorithmic design to allow a successful combination of local search and niching techniques. Six memetic strategies are presented and tested over five population-based algorithms endowed with niching techniques. Experimental results clearly show that local search enhances the performance of the framework for multimodal optimisation in terms of both peak ratio and success rate. The most promising results are obtained by the variants that employ an archive that pre-selects the solutions undergoing local search thus avoiding computational waste. Furthermore, promising results are obtained by variants that reduce the exploitation pressure of the population-based framework by using a simulated annealing logic in the selection process, leaving the exploitation task to the local search.
Handling stagnation through diversity analysis: A new set of operators for evolutionary algorithms

Population size is an important variable in evolutionary algorithms (EA). Its proper configuration improves the performance of the search process not only in terms of the fitness function but also for the resources required. This article introduced a population management mechanism that includes different operators. Such operators are designed and applied based on the diversity of the population. In general terms, the operators address problems in EA regarding stagnation and the inefficient use of the function evaluations. As a case of study, the proposed method is applied in the Differential Evolution (DE) to provide it the ability to change its population size according to its needs. The experimental results and comparisons demonstrate greatly improved performance when compared to the unmodified DE, some of its most successful variants, and other much more complex algorithms from the state-of-the-art.
A Discrete Differential Evolution Algorithm for a Military Fleet Modernization Problem

Differential evolution has a long track record of successfully solving optimization problems in continuous domain due it its powerful Euclidean distance-based learning concept. Although this affects its suitability for solving several problems with permutation variables, several studies show that it can be applicable for effectively solving permutation-based problems. In this paper, an improved design of differential evolution is introduced to solve a military fleet modernization problem with discrete parameters. In this problem, several modernization oper-ations are required to transition a military force from an outdated fleet to a more modern one with the objective of maximizing the force's deployment at the minimum cost over a pre-determined planning period. The proposed differential evolution incorporates a new solution representation, a proposed repairing heuristic method, a modified mutation operator and mapping method for efficiently tackling the discrete characteristics of the targeted problem and is coupled with a simulation model to evaluate the fitness of the generated solutions. To judge its performance, the proposed algorithm has been implemented to solve a case study that addresses recent fleet modernization strategies of the Australian Army to recapitalize its forces over the next decade and in a continual process. The experimental results show that the proposed algorithm can provide more efficient fleet modernization schedules which are 29.32% and 51.43% better than those obtained by other two comparative algorithms.
An Improved Ant Colony Approach for the Competitive Traveling Salesmen Problem

A competitive traveling salesmen problem is a variant of traveling salesman problem in that multiple agents compete with each other in visiting a number of cities. The agent who is the first one to visit a city will receive a reward. Each agent aims to collect as more rewards as possible with the minimum traveling distance. There is still not effective algorithms for this complicated decision making problem. We investigate an improved ant colony approach for the competitive traveling sales-men problem which adopts a time dominance mechanism and a revised pheromone depositing method to improve the quality of solutions with less computational complexity. Simulation results show that the proposed algorithm outperforms the state of art algorithms.
Using density of training data to improve evolutionary algorithms with approximative fitness functions

Evolutionary algorithms are a well-known optimisation technique, especially for non-convex, multi-modal optimisation problems. Their capability of adjusting to different search spaces and tasks by choosing the suitable encoding and operators has led to their widespread use in various application domains. However, application domains sometimes come with difficulties like fitness functions that can not be evaluated or not more than a few times. In these situations, surrogate functions or approximative fitness functions allow the evolutionary algorithm to work despite this complication. Still, using approximative fitness functions comes with a price: The fitness value is no longer correct for every individual, and the algorithm can not know which value to trust. However, statistical methods yield knowledge about the preciseness of the approximation. We propose using this knowledge to adapt the fitness value to ease the effects of the approximative nature. We choose to use the information given in the density of the training data, which has computational merits over the use of other techniques like cross-validation or prediction intervals. We evaluate our method on four well-known benchmark functions and achieve good optimisation success and computation time results.
Quantum- Inspired Structure- Preserving Probabilistic Inference

Probabilistic methods serve as the underlying frame-work of various machine learning techniques. When using these models, a central problem is that of computing the partition function, whose computation is intractable for many models of interest. Here, we present the first quantum-inspired method that is especially designed for computing fast approximations to the partition function. Our approach uses a novel hardware solver for quadratic unconstrained binary optimization problems that relies on evolutionary computation. The specialized design allows us to assess millions of candidate solutions per second, leading to high quality maximum a-posterior (MAP) estimates, even for hard instances. We investigate the expected run-time of our solver and devise new ultra-sparse parity constraints to combine our device with the WISH approximation scheme. A SIMD-like packing strategy further allows us to solve multiple MAP instances at once, resulting in high efficiency and an additional speed-up. Numerical experiments show that our quantum-inspired approach produces accurate and robust results. While pure software implementations of the WISH algorithm typically run on large compute clusters with hundreds of CPUs, our results are achieved on two FPGA boards which both consume below 10 Watts. Moreover, our results extend seamlessly to adiabatic quantum computers.
Multi-Objective Optimization of Sampling Algorithms Pipeline for Unbalanced Problems

The sequencing of sampling algorithms has shown to be a promising approach in generating balanced versions of unbalanced data. Sequencing allows different algorithms of under-sampling and/or over-sampling to be performed in sequence, producing a resulting balanced database. However, defining the most appropriate sequence of sampling algorithms is challenging. This article treats the sequencing problem as a combinatorial optimization task and proposes a multi-objective optimization method to seek promising solutions that maximize the performance of classifiers both in accuracy and in F1-score. The results showed that the proposed method was capable of finding optimized sequences that improved the performance of the classifiers, obtaining statistically better results, mainly in F1- score, when compared with competing methods, in most of the selected unbalanced problems.
Learning to Search Promising Regions by a Monte-Carlo Tree Model

In complex optimization problems, learning where to search is a difficult but critical decision for all search algorithms. Evolutionary computation methods also encounter a dilemma about where to explore or exploit. In this paper, a Monte-Carlo tree is constructed to guide evolutionary algorithms to search multiple promising regions simultaneously. In the Monte-Carlo tree model, a root node that contains all historical solutions represents the whole solution space. In each node of the tree, with k-means clustering method to partition solutions into different groups, group labels of the solutions are used to train support vector regression, which can learn a boundary to partition a region into different sub-regions. According to state values of nodes, reproduction operators of evolutionary algorithms are strengthened by selecting solutions in the most promising regions. From experimental results on multimodal problems, the proposed algorithm shows a competitive performance, which also indicates a great potential for applications to other kinds of optimization problems.
A new kind of hybrid character sums and their applications

In this paper, we introduce a new kind of hybrid character sums and their applications in the constructions of Mutually unbiased bases (MUBs) and codebooks. It is well-known that MUBs have significant applications in quantum information processing and other fields. However, only a few constructions of MUBs are known in the literature. In this note, we provide a recursive constructions of approximately mutually unbiased bases. Meanwhile, we present some constructions of asymptotically optimal codebooks by using the hybrid character sums.
Boolean Functions of Binary Type-II Complementary Array Pair

Binary Type-II complementary pairs are shown to exist only at lengths <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{m}$</tex> , and exhaustive search for binary Type-II complementary sequence pairs of small length shows that the algebraic normal form of these pairs must satisfy specified expression. However, it is still open whether this expression of algebraic normal form determines all the binary Type-II complementary sequence pairs. In this paper, we proved that sequence pairs with such expression must be Type-II complementary sequence pairs, and this expression of algebraic normal form determines all the binary Type-II complementary array pairs of size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\times 2\times\cdots\times 2$</tex>
An Explainable Classifier based on Genetically Evolved Graph Structures

Trusting an algorithmic decision is much easier if it is understood how it was achieved. Therefore, data mining algorithms with explainable abilities are preferred over complex models for critical applications. Rule-based algorithms are among the easiest data classification models to understand. However, as most interpretable models, rule-based algorithms do not provide the highest accuracy. The Attribute-based Decision Graph (AbDG) is a model to represent a labeled data set as a weighted graph over the attributes. When associated with a proper algorithm, AbDGs can be used for supervised data mining tasks. An important aspect of AbDGs is that the graph encompasses the original attribute values and their relationships, which makes it easily interpretable by extracting rules. The AbDG drawback is defining a suitable graph structure for a given data set. In previous works, the authors proposed GA-AbDG, a genetic algorithm to search for an AbDG by evolving its edge set, keeping the vertex set fixed. In this paper, we propose an evolutionary algorithm and genetic operators to evolve both, vertex and edge sets, enhancing the search space of possible AbDG structures. Moreover, we associate a rule-based classifier with the AbDG to achieve explainable results. Experimental results show the proposed method outperforms GA-AbDG and five other classical interpretable classification algorithms.
Summit-assisted Evolutionary Multitasking

Evolutionary computation has served as a blooming research area for decades, in which evolutionary algorithms are inspired from mechanisms of evolution as well as cognitive and social behaviors of creatures in nature as searching processes. An emerging branch of evolutionary computation proposed in recent year is the evolutionary multitasking, which is to manipulate evolutionary algorithms for tackling multitask optimization problems. Recent studies of evolutionary multitasking have drawn much attention on designing effective knowledge transfer mechanisms. This study proposes a novel evolutionary multi-tasking method named summit-assisted evolutionary multitasking (SaEMT) by integrating summit-based knowledge transfer into the general multi-population evolutionary multitasking method. There are two main features in the proposed method, including the summit-based recombination, and the dynamic control of transfer rate. Empirical results show that the proposed method can outperform classical and advanced evolutionary multitasking methods in terms of solution quality and convergence speed. Experimental results also discover that the SaEMT is able to complete in acceptable running time.
Evolutionary Node Layout and Edge Bundling

This paper proposes a method that includes the process of adjusting the position of nodes in edge bundling by integrating an edge bundling and a node layout into a single genetic algorithm. Edge bundling is one of the graph visualization methods that can improve complex graphs and produce visually superior graphs. However, most edge bundling don't include the process of adjusting the position of nodes. Our approaches includes the process of adjusting the position of nodes in edge bundling by integrating an edge bundling and a node layout into genetic algorithm. Then, we apply our method to graphs and evaluate the generated graphs to investigate the effectiveness of our method.
An improved Estimation of Distribution Algorithm for Solving Constrained Mixed-Integer Nonlinear Programming Problems

In a mixed-integer nonlinear programming problem, integer restrictions divide the feasible region into discontinuous feasible parts with different sizes. Evolutionary Algorithms (EAs) are usually vulnerable to being trapped in larger discontinuous feasible parts. In this work, an improved version of an Estimation of Distribution Algorithm (EDA) is developed, where two new op-erations are proposed. The first one establishes a link between the learning-based histogram model and the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon$</tex> -constrained method. Here, the constraint violation level of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon$</tex> -constrained method is used to explore the smaller discontinuous parts and form a better statistical model. The second operation is the hybridization of the EDA with a mutation operator to generate offspring from both the global distribution information and the parent information. A benchmark is used to test the performance of the improved proposal. The results indicated that the proposed approach shows a better performance against other tested EAs. This new proposal solves to a great extent the influence of the larger discontinuous feasible parts, and improve the local refinement of the real variables.