Read by QxMD icon Read

Evolutionary Computation

Emma Hart, Kevin Sim
Although the use of ensemble methods in machine-learning is ubiquitous due to their proven ability to outperform their constituent algorithms, ensembles of optimisation algorithms have received relatively little attention. Existing approaches lag behind machine-learning in both theory and practice, with no principled design guide-lines available. In this paper, we address fundamental questions regarding ensemble composition in optimisation using the domain of bin-packing as a example; in particular we investigate the trade-off between accuracy and diversity, and whether diversity metrics can be used as a proxy for constructing an ensemble, proposing a number of novel metrics for comparing algorithm diversity...
January 10, 2017: Evolutionary Computation
Chao Qian, Yang Yu, Ke Tang, Yaochu Jin, Xin Yao, Zhi-Hua Zhou
In real-world optimization tasks, the objective (i.e., fitness) function evaluation is often disturbed by noise due to a wide range of uncertainties. Evolutionary algorithms are often employed in noisy optimization, where reducing the negative effect of noise is a crucial issue. Sampling is a popular strategy for dealing with noise: to estimate the fitness of a solution, it evaluates the fitness multiple (k) times independently and then uses the sample average to approximate the true fitness. Obviously, sampling can make the fitness estimation closer to the true value, but also increases the estimation cost...
December 16, 2016: Evolutionary Computation
Uday Kamath, Carlotta Domeniconi, Kenneth De Jong
Many real-world problems involve massive amounts of data. Under these circumstances learning algorithms often become prohibitively expensive, making scalability a pressing issue to be addressed. A common approach is to perform sampling to reduce the size of the dataset and enable efficient learning. Alternatively, one customizes learning algorithms to achieve scalability. In either case, the key challenge is to obtain algorithmic efficiency without compromising the quality of the results. In this paper we discuss a meta-learning algorithm (PSBML) which combines concepts from spatially structured evolutionary algorithms (SSEAs) with concepts from ensemble and boosting methodologies to achieve the desired scalability property...
December 16, 2016: Evolutionary Computation
Xinsheng Lai, Yuren Zhou, Xiaoyun Xia, Qingfu Zhang
The Steiner tree problem (STP) aims to determine some Steiner nodes such that the minimum spanning tree over these Steiner nodes and a given set of special nodes has the minimum weight, which is NP-hard. STP includes several important cases. The Steiner tree problem in graphs (GSTP) is one of them. Many heuristics have been proposed for STP, and some of them have proved to be performance guarantee approximation algorithms for this problem. Since evolutionary algorithms (EAs) are general and popular randomized heuristics, it is significant to investigate the performance of EAs for STP...
December 13, 2016: Evolutionary Computation
Samadhi Nallaperuma, Frank Neumann, Dirk Sudholt
Randomized search heuristics are frequently applied to NP-hard combinatorial optimization problems. The runtime analysis of randomized search heuristics has contributed tremendously to their theoretical understanding. Recently, randomized search heuristics have been examined regarding their achievable progress within a fixed time budget. We follow this approach and present a fixed budget analysis for an NP-hard combinatorial optimization problem. We consider the well-known Traveling Salesperson problem (TSP) and analyze the fitness increase that randomized search heuristics are able to achieve within a given fixed time budget...
November 28, 2016: Evolutionary Computation
Md Monjurul Islam, Hemant Kumar Singh, Tapabrata Ray, Ankur Sinha
Bilevel optimization, as the name reflects, deals with optimization at two interconnected hierarchical levels. The aim is to identify the optimum of an upper level leader problem, subject to the optimality of a lower level follower problem. Several problems from the domain of engineering, logistics, economics and transportation have inherent nested structure which requires them to be modeled as bilevel optimization problems. Increasing size and complexity of such problems has prompted active theoretical and practical interest in the design of efficient algorithms for bilevel optimization...
November 7, 2016: 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
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
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
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
Francisco Chicano, Christian Blum, Gabriela Ochoa
No abstract text is available yet for this article.
2016: Evolutionary Computation
Stjepan Picek, Claude Carlet, Sylvain Guilley, Julian F Miller, Domagoj Jakobovic
The role of Boolean functions is prominent in several areas including 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 over the years. There are still many combinations of design criteria left unexplored and in this matter evolutionary computation can play a distinct role. This article concentrates on two scenarios for the use of Boolean functions in cryptography...
2016: Evolutionary Computation
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"