scholarly journals Geometric Generalisation of Surrogate Model-Based Optimisation to Combinatorial and Program Spaces

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yong-Hyuk Kim ◽  
Alberto Moraglio ◽  
Ahmed Kattan ◽  
Yourim Yoon
Keyword(s):  
Evolutionary Algorithms ◽  
Ad Hoc ◽  
Combinatorial Problems ◽  
Basis Functions ◽  
Adaptation Process ◽  
Continuous Problems ◽  
Solution Representation ◽  
Spatial Intuition ◽  
Binary Strings ◽  
Natural Way

Surrogate models (SMs) can profitably be employed, often in conjunction with evolutionary algorithms, in optimisation in which it is expensive to test candidate solutions. The spatial intuition behind SMs makes them naturally suited to continuous problems, and the only combinatorial problems that have been previously addressed are those with solutions that can be encoded as integer vectors. We show how radial basis functions can provide a generalised SM for combinatorial problems which have a geometric solution representation, through the conversion of that representation to a different metric space. This approach allows an SM to be cast in a natural way for the problem at hand, without ad hoc adaptation to a specific representation. We test this adaptation process on problems involving binary strings, permutations, and tree-based genetic programs.

scholarly journals Constraint Problem Specification as Compression

10.29007/7ths ◽  
2018 ◽  
Author(s):  
Steve Prestwich ◽  
S. Armagan Tarim ◽  
Roberto Rossi
Keyword(s):  
Logic Programming ◽  
Data Compression ◽  
Constraint Programming ◽  
Auxiliary Problem ◽  
Combinatorial Problems ◽  
Specification Languages ◽  
Constraint Problem ◽  
High Level ◽  
Natural Way ◽  
Comparable Size

Constraint Programming is a powerful and expressive framework for modelling and solving combinatorial problems. It is nevertheless not always easy to use, which has led to the development of high-level specification languages. We show that Constraint Logic Programming can be used as a meta-language to describe itself more compactly at a higher level of abstraction. This can produce problem descriptions of comparable size to those in existing specification languages, via techniques similar to those used in data compression. An advantage over existing specification languages is that, for a problem whose specification requires the solution of an auxiliary problem, a single specification can unify the two problems. Moreover, using a symbolic representation of domain values leads to a natural way of modelling channelling constraints.

scholarly journals Partial Compilation of ASP Programs

2019 ◽  
Vol 19 (5-6) ◽  
pp. 857-873 ◽  
Author(s):  
BERNARDO CUTERI ◽  
CARMINE DODARO ◽  
FRANCESCO RICCA ◽  
PETER SCHÜLLER
Keyword(s):  
Experimental Analysis ◽  
Ad Hoc ◽  
Blow Up ◽  
State Of The Art ◽  
Answer Set Programming ◽  
General Purpose ◽  
Combinatorial Problems ◽  
New Approach ◽  
Input Program ◽  
Evaluation Algorithm

AbstractAnswer Set Programming (ASP) is a well-known declarative formalism in logic programming. Efficient implementations made it possible to apply ASP in many scenarios, ranging from deductive databases applications to the solution of hard combinatorial problems. State-of-the-art ASP systems are based on the traditional ground&solve approach and are general-purpose implementations, i.e., they are essentially built once for any kind of input program. In this paper, we propose an extended architecture for ASP systems, in which parts of the input program are compiled into an ad-hoc evaluation algorithm (i.e., we obtain a specific binary for a given program), and might not be subject to the grounding step. To this end, we identify a condition that allows the compilation of a sub-program, and present the related partial compilation technique. Importantly, we have implemented the new approach on top of a well-known ASP solver and conducted an experimental analysis on publicly-available benchmarks. Results show that our compilation-based approach improves on the state of the art in various scenarios, including cases in which the input program is stratified or the grounding blow-up makes the evaluation unpractical with traditional ASP systems.

Evolutionary Algorithms for Mobile Ad Hoc Networks

2014 ◽  
Author(s):  
Bernabé Dorronsoro ◽  
Patricia Ruiz ◽  
Grégoire Danoy ◽  
Yoann Pigné ◽  
Pascal Bouvry
Keyword(s):  
Evolutionary Algorithms ◽  
Ad Hoc Networks ◽  
Mobile Ad Hoc Networks ◽  
Ad Hoc ◽  
Mobile Ad Hoc ◽  
Hoc Networks

Kernel-Based Equiprobabilistic Topographic Map Formation

1998 ◽  
Vol 10 (7) ◽  
pp. 1847-1871 ◽  
Author(s):  
Marc M. Van Hulle
Keyword(s):  
Density Estimation ◽  
Ad Hoc ◽  
Receptive Fields ◽  
Learning Rule ◽  
Basis Functions ◽  
Nonparametric Density Estimation ◽  
Topographic Map ◽  
Fixed Topology ◽  
Local Input ◽  
Learning Schemes

We introduce a new unsupervised competitive learning rule, the kernel-based maximum entropy learning rule (kMER), which performs equiprobabilistic topographic map formation in regular, fixed-topology lattices, for use with nonparametric density estimation as well as nonparametric regression analysis. The receptive fields of the formal neurons are overlapping radially symmetric kernels, compatible with radial basis functions (RBFs); but unlike other learning schemes, the radii of these kernels do not have to be chosen in an ad hoc manner: the radii are adapted to the local input density, together with the weight vectors that define the kernel centers, so as to produce maps of which the neurons have an equal probability to be active (equiprobabilistic maps). Both an “online” and a “batch” version of the learning rule are introduced, which are applied to nonparametric density estimation and regression, respectively. The application envisaged is blind source separation (BSS) from nonlinear, noisy mixtures.

scholarly journals A General Approach to Running Time Analysis of Multi-objective Evolutionary Algorithms

2018 ◽  
Author(s):  
Chao Bian ◽  
Chao Qian ◽  
Ke Tang
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Theoretical Aspect ◽  
Combinatorial Problems ◽  
Time Analysis ◽  
Running Time ◽  
Asymptotic Bounds ◽  
Multi Objective ◽  
Running Time Analysis ◽  
Empirical Performance

Evolutionary algorithms (EAs) have been widely applied to solve multi-objective optimization problems. In contrast to great practical successes, their theoretical foundations are much less developed, even for the essential theoretical aspect, i.e., running time analysis. In this paper, we propose a general approach to estimating upper bounds on the expected running time of multi-objective EAs (MOEAs), and then apply it to diverse situations, including bi-objective and many-objective optimization as well as exact and approximate analysis. For some known asymptotic bounds, our analysis not only provides their leading constants, but also improves them asymptotically. Moreover, our results provide some theoretical justification for the good empirical performance of MOEAs in solving multi-objective combinatorial problems.

scholarly journals A Parallel Multi-Objective Cooperative Coevolutionary Algorithm for Optimising Small-World Properties in VANETs

2014 ◽  
Vol 17 (1) ◽  
Author(s):  
Grégoire Danoy ◽  
Julien Schleich ◽  
Pascal Bouvry ◽  
Bernabé Dorronsoro
Keyword(s):  
Evolutionary Algorithms ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Small World ◽  
City Centre ◽  
Nsga Ii ◽  
Solution Vector ◽  
Multi Objective ◽  
Hoc Networks ◽  
Fully Connected

Cooperative coevolutionary evolutionary algorithms differ from standard evolutionary algorithms’ architecture in that the population is split into subpopulations, each of them optimising only a sub-vector of the global solution vector. All subpopulations cooperate by broadcasting their local partial solutions such that each subpopulation can evalu- ate complete solutions. Cooperative coevolution has recently been used in evolutionary multi-objective optimisation, but few works have exploited its parallelisation capabil- ities or tackled real-world problems. This article proposes to apply for the first time a state-of-the-art parallel asynchronous cooperative coevolutionary variant of the non- dominated sorting genetic algorithm II (NSGA-II), named CCNSGA-II, on the injection network problem in vehicular ad hoc networks (VANETs). This multi-objective optimi- sation problem, consists in finding the minimal set of nodes with backend connectivity, referred to as injection points, to constitute a fully connected overlay that will optimise the small-world properties of the resulting network. Recently, the well-known NSGA- II algorithm was used to tackle this problem on realistic instances in the city-centre of Luxembourg. In this work we analyse the performance of the CCNSGA-II when using different numbers of subpopulations, and compare them to the original NSGA-II in terms of both quality of the obtained Pareto front approximations and execution time speedup.

scholarly journals Time for Change: Implementation of Aksentijevic-Gibson Complexity in Psychology

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 948 ◽  
Author(s):  
Aleksandar Aksentijevic ◽  
Anja Mihailovic ◽  
Dragutin T. Mihailovic
Keyword(s):  
Ad Hoc ◽  
Minimum Description Length ◽  
Long Strings ◽  
Probabilistic Methods ◽  
Change Implementation ◽  
Factors Affecting ◽  
Long Time ◽  
Symmetry Perception ◽  
Psychological Processing ◽  
Binary Strings

Given that complexity is critical for psychological processing, it is somewhat surprising that the field was dominated for a long time by probabilistic methods that focus on the quantitative aspects of the source/output. Although the more recent approaches based on the Minimum Description Length principle have produced interesting and useful models of psychological complexity, they have not directly defined the meaning and quantitative unit of complexity measurement. Contrasted to these mathematical approaches are various ad hoc measures based on different aspects of structure, which can work well but suffer from the same problem. The present manuscript is composed of two self-sufficient, yet related sections. In Section 1, we describe a complexity measure for binary strings which satisfies both these conditions (Aksentijevic–Gibson complexity; AG). We test the measure on a number of classic studies employing both short and long strings and draw attention to an important feature—a complexity profile—that could be of interest in modelling the psychological processing of structure as well as analysis of strings of any length. In Section 2 we discuss different factors affecting the complexity of visual form and showcase a 2D generalization of AG complexity. In addition, we provide algorithms in R that compute the AG complexity for binary strings and matrices and demonstrate their effectiveness on examples involving complexity judgments, symmetry perception, perceptual grouping, entropy, and elementary cellular automata. Finally, we enclose a repository of codes, data and stimuli for our example in order to facilitate experimentation and application of the measure in sciences outside psychology.

scholarly journals Automated Algorithm Selection: Survey and Perspectives

2019 ◽  
Vol 27 (1) ◽  
pp. 3-45 ◽  
Author(s):  
Pascal Kerschke ◽  
Holger H. Hoos ◽  
Frank Neumann ◽  
Heike Trautmann
Keyword(s):  
State Of The Art ◽  
Combinatorial Problems ◽  
The State ◽  
Problem Instance ◽  
Ai Planning ◽  
Algorithm Selection ◽  
Automated Algorithm ◽  
Continuous Problems ◽  
Algorithm Configuration ◽  
The One

It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.

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