Abstract: This presentation is about a new design methodology in the context of finding new and innovative design principles by using optimization techniques. The task of innovization stretches the scope beyond an optimization task and attempts to unveil new and innovative design principles relating to decision variables and objectives, so that a deeper understanding of the problem can be obtained. The innovization procedure has been applied to a number of engineering design problems.

Abstract: In the academic world optimization is a crisp, well-defined mathematical topic dealing with accurate objective functions and constraints, where the main research issue is to get to the optimum in the least number of function evaluations (even if it takes a long time to set the problem up). In the engineering world...Optimization is a very different animal - and that is the point of this presentation.

Abstract: Ecomagination is on solving customer's toughest problem thru environment friendly products, the story is about what are they and how the Bangalore center is involved in doing that

Abstract: Many real-world applications of multi-objective optimization involve a large number (10 or more) of objectives. Existing evolutionary multi-objective optimization (EMO) methods are applied only to problems having smaller number of objectives (about five or so) for the task of finding a well-representative set of Pareto-optimal solutions, in a single simulation run. Having adequately shown this task, EMO researchers/practitioners must now investigate if these methodologies can really be used for a large number of objectives. The major impediments in handling large number of objectives relate to stagnation of search process, increased dimensionality of Pareto-optimal front, large computational cost, and difficulty in visualization of the objective space. These difficulties, do and would continue to persist, in "M"-objective optimization problems, having an "M"-dimensional Pareto-optimal front. In this paper, we propose an EMO procedure for solving those large-objective "M" problems, which degenerate to possess a lower-dimensional Pareto-optimal front (lower than "M"). Such problems may often be observed in practice, as ones with similarities in optimal solutions for different objectives - a matter which may not be obvious from the problem description. The proposed method is a principal component analysis (PCA) based EMO procedure, which progresses iteratively from the interior of the search space towards the Pareto-optimal region by adaptively finding the correct lower-dimensional interactions. The efficacy of the procedure is demonstrated by solving up to 30-objective optimization problems.

Abstract: Portfolio optimization is inherently a multi-criteria optimization problem. Although one could imagine various citerias, only return and risk are regarded as relevant conflicting criterias in the classical approach. In that case the pareto-optimal front could be derived analytically, if there were only simple constraints. However, certain investment-laws and inhouse-requirements introduce complex constraints that require the application of MOEAs. Furthermore the problem allows the construction of envelopes for different constraints' specifications. The use of this nice feature may result in improved convergence and will be further investigated.

Abstract: This talk gives a short description of main research actitivities on evolution and learning at Honda Research Instiiute Europe. Selected topics, such as metamodeling in evolutionary optimization, search for robust solutions, dynamic optimization, and multi-objective optimization will be discussed. Theoretic issues as well as application examples will be presented.

Abstract: Although evolutionary algorithms (EAs) have shown several good results in applications, the theoretical work for EAs is still few. Recently, several researchers have started theoretical investigation for EAs, in particular convergence analysis, model of EAs, etc. To our knowledge, theoretical work for population dynamics during optimization was not carried out. We strongly believe that theoretical analysis of population dynamics will not help us only to understand the working mechanism of EAs but also to design an innovative algorithm for tackling optimization problem. This presentation is one of trials to understand population dynamics in genetic algorithms (GAs) with single objective optimization. After modeling GAs, several findings will be discussed in this presentation.

Abstract: Present work shows an optimal orbital trajectory of a spacecraft, from a given departure planet to a given arrival planet. In search of an optimal solution, gravitational power of any planet (swingby) may or may not be considered. Also if swingbys are to be used, then how many times and from which planet each times. The optimization procedure is complex in nature because of the difficulties (non-linearity, highly constrained, and discreteness) associated with the trajectory optimization problem. Hence, a classical algorithm is difficult to apply and also not guaranteed to converge at global optimal solution. Therefore an evolutionary algorithm (EA) strategies are used in the present work. EAs have shown to be successful in solving such problems in other problem domains. A model comprising of multi objective problems is mentioned in the study using combinatorial genetic algorithms.

Abstract: Optimization satisfying a required reliability criterion is a foremost requirement in almost all practical engineering designs. This presentation is aimed at providing a brief review of the various RBDO formulations and methods. A brief description of the inherent concepts is also provided. It also suggests a simplified model of the RBDO problem to work with.

Abstract: We present here a method for optimizing the layout of rectangular parts placed on a rectangular sheet to cut out various objects. We investigate here two types of cutting problems (i) in which guillotine cutting (cutting from edge to edge) is required (mostly metallic sheets where each cut is made individually for one single sheet) and (ii) in which guillotine cutting is not required (cuts which can be made using a punch) i.e. for materials like paper or rubber where the sheets to be cut can be laid side by side or on top of one another and one single cut can be made. For non-guillotine cutting, both real and and binary variable formulations are studied and it is shown that binary variable formulation of the design problem gives better solutions. For guillotine cutting binary variable formulation is studied and it has been shown for known cases that globally optimum solution(s) are obtained.

Abstract: The real world optimization problems are often computationally expensive. The use of statistical techiniques to build approximation of expensive computer analysis code is a possible solution. The metamodels, or model of a model are used to provide such an efficient alternative. In this talk, four such sample approximation techniques, namely response surface methodology, neural networks, inductive learning and kriging are discussed. The recommendations for the appropriate use of statistical approximation technique in the given situation and how common pitfalls can be avoided is briefly discussed.

Abstract: Main idea is to present basics of genomics with blend of bioinformatics and computational biology in layman terminology. Following things are discussed - (1) What are Genes and why it is important to study them? (2) What is microarray and how it is helping in understand various diseases, developmental stages? (3) Few problems where GAs are being applied.

Abstract: We present here a subjective analysis of unary and binary performance indicators. We analyze various advantages and disadvantages of each approach and go on to describe few very common unary performance indicators. Based on the merits and de-merits of these indicators, we then analyze a binary performance indicator viz binary-epsilon indicator.

Abstract: In this talk, a framework for Machine Learning in the game of Iterated Prisoner's Dilemma using Multiobjective Evolutionary Algorithms is presented. In particular, we present the results obtained using the NSGA-II algorithm, and discuss its advantages over the Single Objective Optimization paradigm. We also present the essentials a strategy should have to win in the game of Iterated Prisoner's Dilemma.

Abstract: Due to the vagaries of optimization problems encountered in practice, users resort to different algorithms for solving different optimization problems. In this paper, we suggest an optimization procedure which specializes in solving multi-objective, multi-global problems. The algorithm is carefully designed so as to degenerate to efficient algorithms for solving other simpler optimization problems, such as single-objective uni-global problems, single-objective multi-global problems and multi-objective uni-global problems. The efficacy of the proposed algorithm in solving various problems is demonstrated on a number of test problems. Because of it's efficiency in handling different types of problems with equal ease, this algorithm should find increasing use in real-world optimization problems.