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Evolutionary Computation

Konrad Gizynski, Gerd Gruenert, Peter Dittrich, Jerzy Gorecki
Unconventional computing devices operating on nonlinear chemical media offer an interesting alternative to standard, semiconductor-based computers. In this work we study in-silico a chemical medium composed of communicating droplets that functions as a database classifier. The droplet network can be "programmed" by an externally provided illumination pattern. The complex relationship between the illumination pattern and the droplet behavior makes manual programming hard. We introduce an evolutionary algorithm that automatically finds the optimal illumination pattern for a given classification problem...
October 11, 2016: Evolutionary Computation
Alan J Lockett, Risto Miikkulainen
No Free Lunch (NFL) theorems have been developed in many settings over the last two decades. Whereas NFL is known to be possible in any domain based on settheoretic concepts, probabilistic versions of NFL are presently believed to be impossible in continuous domains. This article develops a new formalization of probabilistic NFL that is sufficiently expressive to prove the existence of NFL in large search domains, such as continuous spaces or function spaces. This formulation is arguably more complicated than its set-theoretic variants, mostly as a result of the numerous technical complications within probability theory itself...
October 4, 2016: Evolutionary Computation
Carola Doerr, Johannes Lengler
Black-box complexity theory provides lower bounds for the runtime of black-box optimizers like evolutionary algorithms and other search heuristics and serves as an inspiration for the design of new genetic algorithms. Several black-box models covering different classes of algorithms exist, each highlighting a different aspect of the algorithms under considerations. In this work we add to the existing black-box notions a new elitist black-box model, in which algorithms are required to base all decisions solely on (the relative performance of) a fixed number of the best search points sampled so far...
October 4, 2016: Evolutionary Computation
Mario A Muñoz, Kate A Smith-Miles
This paper presents a method for the objective assessment of an algorithm's strengths and weaknesses. Instead of examining only the performance of one or more algorithms on a benchmark set, or generating custom problems that maximize the performance difference between two algorithms, our method quantifies both the nature of the test instances and the algorithm performance. Our aim is to gather information about possible phase transitions in performance, i.e., the points in which a small change in problem structure produces algorithm failure...
September 30, 2016: Evolutionary Computation
Fabio Daolio, Arnaud Liefooghe, Sébastien Verel, Hernán Aguirre, Kiyoshi Tanaka
In this paper, we attempt to understand and to contrast the impact of problem features on the performance of randomized search heuristics for black-box multi-objective combinatorial optimization problems. At first, we measure the performance of two conventional dominance-based approaches with unbounded archive on a benchmark of enumerable binary optimization problems with tunable ruggedness, objective space dimension, and objective correlation (ρMNK-landscapes). Precisely, we investigate the expected runtime required by a global evolutionary optimization algorithm with an ergodic variation operator (GSEMO) and by a neighborhood-based local search heuristic (PLS), to identify a [Formula: see text]-approximation of the Pareto set...
September 30, 2016: Evolutionary Computation
Stjepan Picek, Claude Carlet, Sylvain Guilley, Julian F Miller, Domagoj Jakobovic
The role of Boolean functions is prominent in several areas like cryptography, sequences, and coding theory. Therefore, various methods for the construction of Boolean functions with desired properties are of direct interest. New motivations on the role of Boolean functions in cryptography with attendant new properties have emerged during the years. There are still many combinations of design criteria left unexplored and in this matter evolutionary computation can play a distinct role. This paper concentrates on two scenarios for use of Boolean functions in cryptography...
August 2, 2016: Evolutionary Computation
Stjepan Picek, Marko Cupic, Leon Rotim
Substitution Boxes (S-boxes) play an important role in many modern-day cryptographic algorithms, more commonly known as ciphers. Without carefully chosen Sboxes, such ciphers would be easier to break. Therefore, it is not surprising that the design of suitable S-boxes attracts a lot of attention in the cryptography community. The evolutionary computation (EC) community also had several attempts using evolutionary paradigms to evolve S-boxes with good cryptographic properties. This paper focuses on a fitness function one should use when evolving highly nonlinear S-boxes...
August 2, 2016: Evolutionary Computation
V N Coelho, I M Coelho, M J F Souza, T A Oliveira, L P Cota, M N Haddad, N Mladenovic, R C P Silva, F G Guimarães
This paper presents an Evolution Strategy (ES) based algorithm, designed to self-adapt its mutation operators, guiding the search into the solution space using a Self-Adaptive Reduced Variable Neighborhood Search procedure. In view of the specific local search operators for each individual, the proposed population-based approach also fits into the context of the Memetic Algorithms. The proposed variant uses the Greedy Randomized Adaptive Search Procedure with different greedy parameters for generating its initial population, providing an interesting exploration-exploitation balance...
June 3, 2016: Evolutionary Computation
Ahmed Kheiri, Ed Keedwell
Operations research is a well established field that uses computational systems to support decisions in business and public life. Good solutions to operations research problems can make a large difference to the efficient running of businesses and organisations and so the field often searches for new methods to improve these solutions. The high school timetabling problem is an example of an operations research problem and is a challenging task which requires assigning events and resources to time slots subject to a set of constraints...
June 3, 2016: Evolutionary Computation
Maxim Buzdalov, Benjamin Doerr, Mikhail Kever
We analyze the unrestricted black-box complexity of the JUMP function classes for different jump sizes. For upper bounds, we present three algorithms for small, medium and extreme jump sizes. We prove a matrix lower bound theorem which is capable of giving better lower bounds than the classic information theory approach. Using this theorem, we prove lower bounds that almost match the upper bounds. For the case of extreme jump functions, which apart from the optimum only reveal the middle fitness value(s), we use an additional lower bound argument to show that any blackbox algorithm does not gain significant insight about the problem instance from the first [Formula: see text] fitness evaluations...
May 31, 2016: Evolutionary Computation
Emma Hart, Kevin Sim
We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conquer approach in which each heuristic solves a unique subset of the instance set considered. NELLI-GP extends an existing ensemble method called NELLI by introducing a novel heuristic generator that evolves heuristics composed of linear sequences of dispatching rules: each rule is represented using a tree structure and is itself evolved...
April 27, 2016: Evolutionary Computation
Ali Ahrari, Kalyanmoy Deb, Mike Preuss
During the recent decades, many niching methods have been proposed and empirically verified on some available test problems. They often rely on some particular assumptions associated with the distribution, shape and size of the basins, which can seldom be made in practical optimization problems. This study utilizes several existing concepts and techniques, such as taboo points, normalized Mahalanobis distance, and the Ursem's hill-valley function in order to develop a new tool for multimodal optimization, which does not make any of these assumptions...
April 12, 2016: Evolutionary Computation
Mohammad Reza Bonyadi, Zbigniew Michalewicz
This paper reviews recent studies on Particle Swarm Optimization (PSO) algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study.
March 8, 2016: Evolutionary Computation
Paweł Liskowski, Krzysztof Krawiec
In test-based problems, commonly approached with competitive coevolutionary algorithms, the fitness of a candidate solution is determined by the outcomes of its interactions with multiple tests. Usually, fitness is a scalar aggregate of interaction outcomes, and as such imposes a complete order on the candidate solutions. However, passing different tests may require unrelated 'skills', and candidate solutions may vary with respect to such capabilities. In this paper, we provide theoretical evidence that scalar fitness, inherently incapable of capturing such differences, is likely to lead to premature convergence...
March 8, 2016: Evolutionary Computation
I Moser, M Gheorghita, A Aleti
Complex combinatorial problems are most often optimised with heuristic solvers which usually deliver acceptable results without any indication of the quality obtained. Recently, Predictive Diagnostic Optimisation was proposed as a means of characterising the fitness landscape while optimising a combinatorial problem. The scalars produced by Predictive Diagnostic Optimisation appeared to describe the difficulty of the problem with relative reliability. In this study, we record more scalars that may be helpful in determining problem difficulty during the optimisation process and analyse these in combination with other well-known landscape descriptors using exploratory factor analysis on four landscapes that arise from different search operators, applied to a varied set of quadratic assignment problem instances...
February 29, 2016: Evolutionary Computation
Mario Garza-Fabre, Shaun M Kandathil, Julia Handl, Joshua Knowles, Simon C Lovell
Computational approaches to de novo protein tertiary structure prediction, including those based on the preeminent 'fragment-assembly' technique, have failed to scale up fully to larger proteins (of the order of 100 residues and above). A number of limiting factors are thought to contribute to the scaling problem over and above the simple combinatorial explosion, but the key ones relate to the lack of exploration of properly diverse protein folds, and an acute form of 'deception' in the energy function whereby low-energy conformations do not reliably equate with native structures...
February 23, 2016: Evolutionary Computation
Rubén Saborido, Ana B Ruiz, Mariano Luque
In this paper, we propose a new evolutionary algorithm for multiobjective optimization called Global WASF-GA (Global Weighting Achievement Scalarizing Function Genetic Algorithm), which falls within the aggregation-based evolutionary algorithms. The main purpose of Global WASF-GA is to approximate the whole Pareto optimal front. Its fitness function is defined by an achievement scalarizing function (ASF) based on the Tchebychev distance, in which two reference points are considered (an utopian and the nadir objective vectors), and where the weight vector used is taken from a set of weight vectors whose inverses are well-distributed...
February 8, 2016: Evolutionary Computation
Iztok Fajfar, Janez Puhan, Árpád Bűrmen
We use genetic programming to evolve a direct search optimization algorithm, similar to that of the standard downhill simplex optimization method proposed by Nelder and Mead (1965). In training process, we use several 10-dimensional quadratic functions with randomly displaced parameters and different randomly generated starting simplices. The genetically obtained optimization algorithm shows overall better performance than the original Nelder-Mead method on a standard set of test functions. We observe that many parts of the genetically produced algorithm are seldom or never executed, which allows us to greatly simplify the algorithm by removing the redundant parts...
January 25, 2016: Evolutionary Computation
A Nguyen, J Yosinski, J Clune
The Achilles Heel of stochastic optimization algorithms is getting trapped on local optima. Novelty Search mitigates this problem by encouraging exploration in all interesting directions by replacing the performance objective with a reward for novel behaviors. This reward for novel behaviors has traditionally required a human-crafted, behavioral distance function. While Novelty Search is a major conceptual breakthrough and outperforms traditional stochastic optimization on certain problems, it is not clear how to apply it to challenging, high-dimensional problems where specifying a useful behavioral distance function is difficult...
2016: Evolutionary Computation
Andreia P Guerreiro, Carlos M Fonseca, Luís Paquete
Given a nondominated point set [Formula: see text] of size [Formula: see text] and a suitable reference point [Formula: see text], the Hypervolume Subset Selection Problem (HSSP) consists of finding a subset of size [Formula: see text] that maximizes the hypervolume indicator. It arises in connection with multiobjective selection and archiving strategies, as well as Pareto-front approximation postprocessing for visualization and/or interaction with a decision maker. Efficient algorithms to solve the HSSP are available only for the 2-dimensional case, achieving a time complexity of [Formula: see text]...
2016: Evolutionary Computation
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