Dominance-Based Pareto-Surrogate for Multi-Objective Optimization
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Most surrogate approaches to multi-objective optimization build a surrogate model for each objective. These surrogates can be used inside a classical Evolutionary Multiobjective Optimization Algorithm (EMOA) in lieu of the actual objectives, without modify ...
Discrete optimization is a difficult task common to many different areas in modern research. This type of optimization refers to problems where solution elements can assume one of several discrete values. The most basic form of discrete optimization is bin ...
This paper introduces a multi-objective EA, termed the Clustering Pareto Evolutionary Algorithm (CPEA). The CPEA finds and retains many local Pareto- optimal fronts, rather than just the global front as is the case of most multi- objective EAs found in the ...
Energy-efficient design of multimedia embedded systems demands optimizations in both hardware and software. Software optimization has no received much attention, although modern multimedia applications exhibit high resource utilization. In order to efficie ...
In this thesis, we focus on standard classes of problems in numerical optimization: unconstrained nonlinear optimization as well as systems of nonlinear equations. More precisely, we consider two types of unconstrained nonlinear optimization problems. On t ...
Machine Learning is a modern and actively developing field of computer science, devoted to extracting and estimating dependencies from empirical data. It combines such fields as statistics, optimization theory and artificial intelligence. In practical task ...
This paper illustrates a methodology developed in order to facilitate the analysis of complex systems characterized by a large number of technical, economical and environmental parameters. Thermo-economic modeling of a natural gas combined cycle including ...
Machine Learning is a modern and actively developing field of computer science, devoted to extracting and estimating dependencies from empirical data. It combines such fields as statistics, optimization theory and artificial intelligence. In practical task ...
This thesis presents the development of a new multi-objective optimisation tool and applies it to a number of industrial problems related to optimising energy systems. Multi-objective optimisation techniques provide the information needed for detailed anal ...
The report deals with the novel application of Support Vector Machines (Support Vectore Classification and Support Vector Regression) for the analysis and modelling of reservoir data. 2 problems are considered: classification and mapping of porosity data. ...