This paper studies two landscapes of different instances of the 0-1 knapsack problem. The instances are generated randomly from varied weight distributions. We show that the variation of the weights can be used to guide the selection of the most ...

This work applies Grammatical Evolution to identify taxonomic hierarchies of concepts from Wikipedia. Each article in Wikipedia covers a concept and is cross-linked by hyperlinks that connect related concepts. Hierarchical taxonomies and their ...

In an optimization framework, some criteria might be more relevant than others; the internal computational cost of the optimization algorithm might be negligible or not; the quality of intermediate search points might be important or not. For this ...

Computational material discovery can search large design space to identify promising candidates for experimental material design. Density Functional Theory (DFT) based first principle calculation has been able to calculate many electrical and physical ...

This paper introduces two surrogate models for continous black-box optimization, Gaussian processes and random forests, as an alternative to the already used ordinal SVM regression. We employ the CMA-ES as the reference optimization method with which ...

We present an approach for the automatic solving of the scheduling and allocation problem in multicore environments, as well as assigning optimal voltage and frequency levels to each core, using a multiobjective evolutionary algorithm (EA) where both ...

In this paper, we address the problem of black box optimization over binary vectors. We introduce a novel evolutionary algorithm called Herding Evolutionary Algorithm which relies on herding to generate individuals with empirical moments close to those ...

The correctness of program verification systems is of great importance, since they are used to formally prove that safety- and security-critical programs follow their specification. Within these verification systems, the background axiomatization ...

In search-based structural testing, meta-heuristic search techniques have been frequently used to automate test data generation. In this paper, we introduce the use of novelty search algorithm to the test data generation problem based on statement-...

GPU computing has spread its capacity over most of the scientific computing areas. Soft computing is aware of the potential of this computing architecture. In order to achieve high performance, practitioners have to deal with the particularities ...

Several real world problems have two levels of optimization instead of a single one. These problems are said to be bi-level and are so computationally expensive to solve since the evaluation of each upper level solution requires finding an optimal ...

Global Sensitivity Analysis (GSA) studies how uncertainty in the inputs of a system influences uncertainty in its outputs. GSA is extensively used by experts to gather information about the behavior of models, through computationally-intensive ...

Dynamically adding sensors to the Extended Classifier System (XCS) during its learning process in multistep problems has been demonstrated feasible by using messy coding (XCSm) and s-expressions (XCSL) as the representation of classifier conditions. ...

Increasing availability of multiple processing elements on the recent desktop and personal computers poses unavoidable challenges in realizing their processing power. The challenges include programming these high processing elements. Parallel ...

We introduce the concept of probabilistic program evaluation, whereby the order in which the statements of a proposed program are executed, and whether individual statements are executed at all, are controlled by probability distributions associated ...

This paper expands on the research presented in [12] by comparing the performance of genetically evolved programs operating under dynamic game environments with that of neural networks with evolved weights. On the genetic programming side, the maximum ...

We consider noisy optimization and some traditional variance reduction techniques aimed at improving the convergence rate, namely (i) common random numbers (CRN), which is relevant for population-based noisy optimization and (ii) stratified sampling, ...

A new iterative heuristic algorithm, based on Multi Swarm Optimization, is presented for Steiner Tree Problems (STP) in the 2-dimensional Euclidean plane. The basic algorithm is made practical for large instances by applying a result from graph theory, ...

In learning from demonstration (LfD) a human trainer demonstrates desired behaviors to a robotic agent, creating a training set that the agent can learn from. LfD allows non-programmers to easily and naturally train robotic agents to perform specific ...

Embedded systems, mainly battery-powered, must guarantee low power consumption and good performance. The cache memory design is crucial in embedded systems. Given the high number of parameters and their ranges of values, the exhaustive exploration of ...

Architecture-based software performance optimization can not only significantly save time but also reduce cost. At present, researchers have proposed rule-based and metaheuristic-based approaches. Metaheuristic-based approaches can only explore a few of ...

Swarm Intelligence metaheuristics, and among them Ant Colony Optimization (ACO), have been successfully applied worldwide to solve multiple examples of combinatorial NP-hard problems, giving good solutions in a reasonable period of time (an essential ...

A dynamic version of the Inverse Kinematics problem addresses the two main objectives: One objective is to find a configuration of joints such that a desired pose and orientation can be reached by a robotic arm. Another one is to preserve this state in ...

In recent years, many evolutionary algorithms approaches were introduced to improve existing algorithms as well as to solve optimization and search for problems. Problems involving the optimization of many objectives require a set of optimal solutions ...

Increased demands for energy are driving development of new technologies for power generation with high efficiency. Direct fired fuel cell turbine hybrid systems are one such development, which promise to drastically increase power generation efficiency,...

Using evolutionary algorithms to generate a diverse set of solutions where all of them meet a given quality criteria has gained increasing interest in recent years. In order to gain theoretical insights on the working principle of population-based ...

We propose a genetic algorithm (GA) for solving the maximization version of the Optimal Linear Arrangement problem and we also demonstrate how solutions found by it can be used for constructing smart initial layouts for force-directed graph drawing. ...

Time series analysis is an important research topic in science and engineering. Real-world time series are usually non-stationary with time-varying parameters. Identification of non-stationary time series with a switched model includes finding the ...

Generally, we distinguish between two classes of hyper-heuristic approaches, heuristic selection and heuristic generation. The former one works with existing heuristics and tries to find their optimal order for solving the instance. The later approach ...

Memetic Algorithms are intrinsically concerned with exploiting all available knowledge about the problem under study. The incorporation of problem domain knowledge is not an optional mechanism, but a fundamental feature of the MAs. In this paper we ...

Distributed Adaptive Metaheuristics Selection (DAMS) is a framework dedicated to adaptive optimization in distributed environments. We investigate the design of adaptive strategies allowing to control the local selection of metaheuristics and to ...

A new community structure measure called Surprise has been proposed to address the resolution limit problem of modularity. However, our analysis shows that, similar to modularity, Surprise also suffers from the so-called extreme degeneracy problem, ...

This paper aims at investigating platform-independent measures to determine the efficiency of a load-balancer. Counterpoised curves are proposed as a mean to counterweight the utilization of resources with a penalty due to the migration of tasks. As a ...

This study exhaustively compares the abilities to solve many-objective problems of eight representative algorithms from four different classes (i.e., Pareto-, aggregation-, indicator-, and diversity-based EMO algorithms). The eight compared algorithms ...

Mating scheme is one of the key operations in genetic algorithms. In this paper, we propose an adaptive mating method combining Hungarian mating schemes that have been previously suggested. Hungarian mating schemes include i) minimizing the sum of ...

Software product line (SPL) scoping is important for planning upfront investment. One challenge with scoping comes from inaccuracies in estimated parameters and uncertainty in environment. In this paper, a method to incorporate uncertainty in SPL ...

We present two enhancements to the local search strategy for Incremental Ant Colony Algorithm (IACO_R), that uses Multi-Trajectory Local Search (Mtsls1) as the exploitation engine. First, a new method to handle bound constraints and a modified ...

Resources scheduling is a significant research topic in cloud computing, which is often modeled as a cost-minimization and deadline-constrained workflow scheduling model. This is a constrained single objective problem that to minimize the overall ...

We show that multistart next ascent hillclimbing compares favourably to crowding-based genetic algorithms when solving instances of the multimodal problem generator. We conjecture that it is unlikely that any practical evolutionary algorithm is capable ...

The water distribution network (WDN) is the most vulnerable part of a water system due to the large number of access points, requiring a reliable monitoring and surveillance system to timely detect contamination events. A multi-objective evolutionary ...

We present a Particle Swarm Optimizer for generating boolean functions with good cryptographic properties. The proposed algorithm updates the particles positions while preserving their Hamming weights, to ensure that the generated functions are balanced,...

We propose a post-processing strategy which consists of applying the averaged Hausdorff indicator to the complete archive of solutions after optimization by multiobjective estimation of distribution algorithms (MEDAs) to select a uniformly distributed ...

Many important problem classes lead to large variations in fitness evaluation times, such as is often the case in Genetic Programming where the time complexity of executing one individual may differ greatly from that of another. Asynchronous Parallel ...

Computer assisted human creativity encodes human design decisions in algorithms allowing machines to produce artwork variants. Based on this automated production, one can leverage collective understanding of beauty to rank computer-generated artworks ...

Since the early studies in multiobjective evolutionary optimization, fitness assignment and/or selection have been the main focus of research in the field. In general, many of the methods proposed share the same goals: keep diversity along the Pareto-...

This work introduces Wave, a divide and conquer approach to GP whereby a sequence of short, and dependent but potentially heterogeneous GP runs provides a collective solution; the sequence akins wave such that each short GP run is a period of the wave. ...