Self-adaptation of mutation distribution in evolution strategies for dynamic optimization problems

Copyright @ IOS Press. All Rights Reserved.Evolution strategies with q-Gaussian mutation, which allows the self-adaptation of the mutation distribution shape, is proposed for dynamic optimization problems in this paper. In the proposed method, a real parameter q, which allows to smoothly control the shape of the mutation distribution, is encoded in the chromosome of the individuals and is allowed to evolve. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutation on experiments generated from the simulation of evolutionary robots and on dynamic optimization problems generated by the Moving Peaks generator

Use of the q-Gaussian mutation in evolutionary algorithms

Copyright @ Springer-Verlag 2010.This paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed method, the real parameter q of the q-Gaussian mutation is encoded in the chromosome of individuals and hence is allowed to evolve during the evolutionary process. In order to test the new mutation operator, evolution strategy and evolutionary programming algorithms with self-adapted q-Gaussian mutation generated from anisotropic and isotropic distributions are presented. The theoretical analysis of the q-Gaussian mutation is also provided. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutations in the optimization of a set of test functions. Experimental results show the efficiency of the proposed method of self-adapting the mutation distribution in evolutionary algorithms.This work was supported in part by FAPESP and CNPq in Brazil and in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant EP/E060722/1 and Grant EP/E060722/2

Classification of landforms in Southern Portugal (Ria Formosa Basin)

A Geographic Information Systems-based tool is used for macro-landform classification following the Hammond procedure, based upon a Digital Terrain Model (DTM) created from ordinary Kriging. Gentle slopes, surface curvature, highlands and lowlands areas are derived from the DTM. Combining this information allows the classification of terrain units (landforms). The procedure is applied to the Ria Formosa basin (Southern Portugal), with five different terrain types classified (plains, tablelands, plains with hills, open hills and hills)

Improving a branch-and-bound approach for the degree-constrained minimum spanning tree problem with LKH

The degree-constrained minimum spanning tree problem, which involves finding a minimum spanning tree of a given graph with upper bounds on the vertex degrees, has found multiple applications in several domains. In this paper, we propose a novel CP approach to tackle this problem where we extend a recent branch-and-bound approach with an adaptation of the LKH local search heuristic to deal with trees instead of tours. Every time a solution is found, it is locally optimised by our new heuristic, thus yielding a tightened cut. Our experimental evaluation shows that this significantly speeds up the branch-and-bound search and hence closes the performance gap to the state-of-the-art bottom-up CP approach

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.FAPESPFAPESPRUSPRUS

An evolutionary approach to practical constraints in scheduling: a case-study of the wine bottling problem

Practical constraints associated with real-world problems are a key differentiator with respect to more artificially formulated problems. They create challenging variations on what might otherwise be considered as straightforward optimization problems from an evolutionary computation perspective. Through solving various commercial and industrial problems using evolutionary algorithms, we have gathered experience in dealing with practical dynamic constraints. Here, we present proven methods for dealing with these issues for scheduling problems. For use in real-world situations, an evolutionary algorithm must be designed to drive a software application that needs to be robust enough to deal with practical constraints in order to meet the demands and expectations of everyday use by domain specialists who are not necessarily optimization experts. In such situations, addressing these issues becomes critical to success. We show how these challenges can be dealt with by making adjustments to genotypic representation, phenotypic decoding, or the evaluation function itself. The ideas presented in this chapter are exemplified by the means of a case study of a real-world commercial problem, namely that of bottling wine in a mass-production environment. The methods described have the benefit of having been proven by a full-fledged implementation into a software application that undergoes continual and vigorous use in a live environment in which time-varying constraints, arising in multiple different combinations, are a routine occurrence.Arvind Mohais, Sven Schellenberg, Maksud Ibrahimov, Neal Wagner, and Zbigniew Michalewic