Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study

Proceedings of: 5th International Conference, LION 5, Rome, Italy, January 17-21, 2011.The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have a intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work we argue that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and an hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.This work was supported by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02.Publicad

Using Comparative Preference Statements in Hypervolume-Based Interactive Multiobjective Optimization

International audienceThe objective functions in multiobjective optimization problems are often non-linear, noisy, or not available in a closed form and evolutionary multiobjective optimization (EMO) algorithms have been shown to be well applicable in this case. Here, our objective is to facilitate interactive decision making by saving function evaluations outside the "interesting" regions of the search space within a hypervolume-based EMO algorithm. We focus on a basic model where the Decision Maker (DM) is always asked to pick the most desirable solution among a set. In addition to the scenario where this solution is chosen directly, we present the alternative to specify preferences via a set of so-called comparative preference statements. Examples on standard test problems show the working principles, the competitiveness, and the drawbacks of the proposed algorithm in comparison with the recent iTDEA algorithm

Decisional Conflict and User Acceptance of Multicriteria Decision-Making Aids *

Despite the development of increasingly sophisticated and refined multicriteria decision-making (MCDM) methods, an examination of the experimental evidence indicates that users most often prefer relatively unsophisticated methods. In this paper, we synthesize theories and empirical findings from the psychology of judgment and choice to provide a new theoretical explanation for such user preferences. Our argument centers on the assertion that the MCDM method preferred by decision makers is a function of the degree to which the method tends to introduce decisional conflict. The model we develop relates response mode, decision strategy, and the salience of decisional conflict to user preferences among decision aids. We then show that the model is consistent with empirical results in MCDM studies. Next, the role of decisional conflict in problem formulation aids is briefly discussed. Finally, we outline future research needed to thoroughly test the theoretical mechanisms we have proposed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73461/1/j.1540-5915.1991.tb00371.x.pd

SUPPLEMENTARY TOOLS TO MEASURE AND UNDERSTAND THE FLOW IN THE CONTINUOUS CASTING MOULD

For the production of clean steel it is important to control the flow in the mould. A lot of modelling workhas already been done to study the flow behaviour in the mould. Measurements in the continuous castingmould are necessary to validate the modelling work, especially when multiphase flow is studied or whenelectromagnetic flow control systems are incorporated. For a few years, two measuring principles have beenused to study the flow behaviour at the meniscus in the mould at Corus IJmuiden No.2 BOS. The mainmethod is a simple flow measuring device, which consists of a refractory tube sealed at the bottom and fixedat a pivoting point at the top. The tube is submerged in the liquid steel in the mould. Measuring the resultinginclination angle reveals the flow direction and gives a measure of the velocity. In this way the effect of castingparameters on flow behaviour can be studied, such as the effect of the submersion depth of the SEN, the Arflowon the shrouding system (between the tundish and mould) and the change from double roll to single rollflow in the mould. Also, measurements are done with the ‘nail board’ method giving a snapshot of the flowdirection at multiple locations in the meniscus. An electromagnetic sensor was used to measure the multiphaseflow condition in the SEN. In this paper results of these measurements are shown and examples are comparedwith CFD calculations. A better understanding of the effect of casting parameters on the flow pattern in themould was obtained using these supplementary tools

Visualising Evolution History in Multi- and Many-Objective Optimisation

Evolutionary algorithms are widely used to solve optimisation problems. However, challenges of transparency arise in both visualising the processes of an optimiser operating through a problem and understanding the problem features produced from many-objective problems, where comprehending four or more spatial dimensions is difficult. This work considers the visualisation of a population as an optimisation process executes. We have adapted an existing visualisation technique to multi- and many-objective problem data, enabling a user to visualise the EA processes and identify specific problem characteristics and thus providing a greater understanding of the problem landscape. This is particularly valuable if the problem landscape is unknown, contains unknown features or is a many-objective problem. We have shown how using this framework is effective on a suite of multi- and many-objective benchmark test problems, optimising them with NSGA-II and NSGA-III

Cytogenetic analysis of HER1/EGFR, HER2, HER3 and HER4 in 278 breast cancer patients

INTRODUCTION: The HER (human EGFR related) family of receptor tyrosine kinases (HER1/EGFR (epidermal growth factor receptor)/c-erbB1, HER2/c-erbB2, HER3/c-erbB3 and HER4/c-erbB4) shares a high degree of structural and functional homology. It constitutes a complex network, coupling various extracellular ligands to intracellular signal transduction pathways resulting in receptor interaction and cross-activation. The most famous family member is HER2, which is a target in Herceptin therapy in metastatic status and also in adjuvant therapy of breast cancer in the event of dysregulation as a result of gene amplification and resulting protein overexpression. The HER2-related HER receptors have been shown to interact directly with HER2 receptors and thereby mutually affect their activity and subsequent malignant growth potential. However, the clinical outcome with regard to total HER receptor state remains largely unknown. METHODS: We investigated HER1-HER4, at both the DNA and the protein level, using fluorescence in situ hybridisation (FISH) probes targeted to all four receptor loci and also immunohistochemistry in tissue microarrays derived from 278 breast cancer patients. RESULTS: We retrospectively found HER3 gene amplification with a univariate negative impact on disease-free survival (hazard ratio 2.35, 95% confidence interval 1.08 to 5.11, p = 0.031), whereas HER4 amplification showed a positive trend in overall and disease-free survival. Protein expression revealed no additional information. CONCLUSION: Overall, the simultaneous quantification of HER3 and HER4 receptor genes by means of FISH might enable the rendering of a more precise stratification of breast cancer patients by providing additional prognostic information. The continuation of explorative and prospective studies on all HER receptors will be required for an evaluation of their potential use for specific therapeutic targeting with respect to individualised therapy

Investigating the influence of product perception and geometric features

Research in emotional design and Kansei Engineering has shown that aesthetics play a significant role in the appeal of a product. This paper contributes to establishing a methodology to identify the relationships between perceptions, aesthetic features, desire to own and background of consumers. Surveys were conducted with 71 participants to gather their perceptions of 11 vase concepts. Advanced statistical analyses, including mixed models, were applied to allow generalisation of the results beyond the data sample. Significant relations between the desire to own a product and how the product is perceived were found (the desire to own was found to be related to beautiful, expensive, elegant, exciting, feminine, common and dynamic vases), as well as between the perceptions and the parameters describing the form of the vases (a vase was perceived as beautiful if it had many curved lines and was simple and tall). An automated mixed model analysis was conducted and revealed that general rules can be found between aesthetic features, perceptions and ownership, which can apply across gender and culture. The findings include design rules that link aesthetic features with perceptions. These contribute to research as guidelines for design synthesis and can either be implemented via shape grammars or parametric modelling approaches. These rules are also interesting for 3D printing applications, especially important when the consumer is the designer. Some of these design rules are linked to the desire to own a product, they have implications for industry, and they offer guidelines to creating attractive products that people want to own

Not all parents are equal for MO-CMA-ES

International audienceThe Steady State variants of the Multi-Objective Covariance Matrix Adaptation Evolution Strategy (SS-MO-CMA-ES) generate one offspring from a uniformly selected parent. Some other parental selection operators for SS-MO-CMA-ES are investigated in this paper. These operators involve the definition of multi-objective rewards, estimating the expectation of the offspring survival and its Hypervolume contribution. Two selection modes, respectively using tournament, and inspired from the Multi-Armed Bandit framework, are used on top of these rewards. Extensive experimental validation comparatively demonstrates the merits of these new selection operators on unimodal MO problems