A novel population-based multi-objective CMA-ES and the impact of different constraint handling techniques

htmlabstractThe Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is a well-known, state-of-the-art optimization algorithm for single-objective real-valued problems, especially in black-box settings. Although several extensions of CMA-ES to multi-objective (MO) optimization exist, no extension incorporates a key component of the most robust and general CMA-ES variant: the association of a population with each Gaussian distribution that drives optimization. To achieve this, we use a recently introduced framework for extending population-based algorithms from single- to multi-objective optimization. We compare, using six well-known benchmark problems, the performance of the newly constructed MO-CMA-ES with existing variants and with the estimation of distribution algorithm (EDA) known as iMAMaLGaM, that is also an instance of the framework, extending the single-objective EDA iAMaLGaM to MO. Results underline the advantages of being able to use populations. Because many real-world problems have constraints, we also study the use of four constraint-handling techniques. We find that CMA-ES is typically less robust to these techniques than iAMaLGaM. Moreover, whereas we could verify that a penalty method that was previously used in literature leads to fast convergence, we also find that it has a high risk of finding only nearly, but not entirely, feasible solutions. We therefore propose that other constraint-handling techniques should be preferred in general

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

Preference Articulation by Means of the R2 Indicator

International audienceIn multi-objective optimization, set-based performance indicators have become the state of the art for assessing the quality of Pareto front approximations. As a consequence, they are also more and more used within the design of multi-objective optimization algorithms. The R2 and the Hypervolume (HV) indicator represent two popular examples. In order to understand the behavior and the approximations preferred by these indicators and algorithms, a comprehensive knowledge of the indicator's properties is required. Whereas this knowledge is available for the HV, we presented a first approach in this direction for the R2 indicator just recently. In this paper, we build upon this knowledge and enhance the considerations with respect to the integration of preferences into the R2 indicator. More specifically, we analyze the effect of the reference point, the domain of the weights, and the distribution of weight vectors on the optimization of μ solutions with respect to the R2 indicator. By means of theoretical findings and empirical evidence, we show the potentials of these three possibilities using the optimal distribution of μ solutions for exemplary setups

Influence of electron correlations on ground-state properties of III-V semiconductors

Lattice constants and bulk moduli of eleven cubic III-V semiconductors are calculated using an ab initio scheme. Correlation contributions of the valence electrons, in particular, are determined using increments for localized bonds and for pairs and triples of such bonds; individual increments, in turn, are evaluated using the coupled cluster approach with single and double excitations. Core-valence correlation is taken into account by means of a core polarization potential. Combining the results at the correlated level with corresponding Hartree-Fock data, we obtain lattice constants which agree with experiment within an average error of -0.2%; bulk moduli are accurate to +4%. We discuss in detail the influence of the various correlation contributions on lattice constants and bulk moduli.Comment: 4 pages, Latex, no figures, Phys. Rev. B, accepte

The geometry of nonlinear least squares with applications to sloppy models and optimization

Parameter estimation by nonlinear least squares minimization is a common problem with an elegant geometric interpretation: the possible parameter values of a model induce a manifold in the space of data predictions. The minimization problem is then to find the point on the manifold closest to the data. We show that the model manifolds of a large class of models, known as sloppy models, have many universal features; they are characterized by a geometric series of widths, extrinsic curvatures, and parameter-effects curvatures. A number of common difficulties in optimizing least squares problems are due to this common structure. First, algorithms tend to run into the boundaries of the model manifold, causing parameters to diverge or become unphysical. We introduce the model graph as an extension of the model manifold to remedy this problem. We argue that appropriate priors can remove the boundaries and improve convergence rates. We show that typical fits will have many evaporated parameters. Second, bare model parameters are usually ill-suited to describing model behavior; cost contours in parameter space tend to form hierarchies of plateaus and canyons. Geometrically, we understand this inconvenient parametrization as an extremely skewed coordinate basis and show that it induces a large parameter-effects curvature on the manifold. Using coordinates based on geodesic motion, these narrow canyons are transformed in many cases into a single quadratic, isotropic basin. We interpret the modified Gauss-Newton and Levenberg-Marquardt fitting algorithms as an Euler approximation to geodesic motion in these natural coordinates on the model manifold and the model graph respectively. By adding a geodesic acceleration adjustment to these algorithms, we alleviate the difficulties from parameter-effects curvature, improving both efficiency and success rates at finding good fits.Comment: 40 pages, 29 Figure

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Similarities in network segmentations were evaluated using pairwise Dice correlations. Articular cartilage thickness was computed per-scan and longitudinally. Correlation between thickness error and segmentation metrics was measured using Pearson's coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives. Results: Six teams (T1-T6) submitted entries for the challenge. No significant differences were observed across all segmentation metrics for all tissues (p=1.0) among the four top-performing networks (T2, T3, T4, T6). Dice correlations between network pairs were high (>0.85). Per-scan thickness errors were negligible among T1-T4 (p=0.99) and longitudinal changes showed minimal bias (<0.03mm). Low correlations (<0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top performing networks (p=1.0). Empirical upper bound performances were similar for both combinations (p=1.0). Conclusion: Diverse networks learned to segment the knee similarly where high segmentation accuracy did not correlate to cartilage thickness accuracy. Voting ensembles did not outperform individual networks but may help regularize individual models.Comment: Submitted to Radiology: Artificial Intelligence; Fixed typo

Pemetaan dan Penggambaran Gua Tebing Mandu Tontonan sebagai Objek Situs Wisata dan Prasejarah di Kabupaten Enrekang

Kabupaten Enrekang merupakan salah satu daerah yang terletak di bagian utara Sulawesi Selatan yang memiliki wilayah perbukitan dan pegunungan. Potensi temuan-temuan prasejarah di Enrekang diperoleh dari serangkaian kegiatan survei permukaan dengan teknik pencuplikan sampel yang menunjukkan ciri-ciri teknologi prasejarah. Temuan-temuan survey adalah lukisan cap tangan di dinding tebing karst, gua-gua dengan temuan artefak batu,tulang, tembikar dan wadah kubur dari kayu yang disebut mandu atau duni. Selain itu juga ditemukan situs megalitik di atas puncak gunung yang memiliki peninggalan seperti lumping batu, fragmen tembikar dan susunan batu yang merupakan pembatas daerah permukiman. Secara makro, Penelitian ini bertujuan untuk mengetahui sebaran situs-situs prasejarah dalam rangka memahami karakter budaya Enrekang. Hasil penelitian menunjukkan bahwa. Enrekang memiliki diversitas budaya prasejarah yang memiliki aksesibiltas dengan sumber daya alam yang sekaligus menunjang permukiman manusia masa praneolitik hingga persentuhan budaya Austronesia sekitar 3,500 tahun yang lalu dengan pemanfaatan sumber alamnya. Kawasan situs objek wisata Tebing Mandu merupakan salah satu objek wisata sejarah di kabupaten Enrekang tepatnya di pinggir sungai mata allo kelurahan Tanete kecamtan Anggeraja kabupaten Enrekang. Tebing yang menjulang tinggi ini terbentuk secara alami di perkirakan memiliki ketinggian kurang lebih 180 meter dengan panjang 200 meter. Tebing yang berdiri kokoh ini memiliki keunikan tersendiri, dimana terdapat Tebing yang menjulang vertikal dan tegak lurus setinggi 100 meter terdapat teras untuk meletakkan peti jenazah yang mirip dengan peti (erong) yang ada di Toraja.Pada tebing Terdapat lubang panjang sekitar 50 meter di atas sungai mata allo. Di dalam lubang berjejer rapipeti mati yang terbuat dari kayu yang hampir menyerupai perahu, di dalam peti masih terdapat tengkorak-tengkorak manusia. Pada daerah menara karst di Tontonan, Kec. Anggeraja Kab. Enrekang merupakan daerah pemakaman dari para leluhur pada zaman peperangan. Sungai Tontonan yang mengalir di bawahnya menambah agung keperkasaan tebing Mandu Tontonan.&nbsp; Menurut cerita yang beredar, kuburan batu ini masih memiliki hubungan erat dengan manusia pertama yang mendiami pulau Sulawesi yang sekarang bermukim di Tanah Toraja. Ada yang beranggapan bahwa, makam yang nangkring di tebing ini merupakan makam leluhur orang Toraja

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

A multi-tier adaptive grid algorithm for the evolutionary multi-objective optimisation of complex problems

The multi-tier Covariance Matrix Adaptation Pareto Archived Evolution Strategy (m-CMA-PAES) is an evolutionary multi-objective optimisation (EMO) algorithm for real-valued optimisation problems. It combines a non-elitist adaptive grid based selection scheme with the efficient strategy parameter adaptation of the elitist Covariance Matrix Adaptation Evolution Strategy (CMA-ES). In the original CMA-PAES, a solution is selected as a parent for the next population using an elitist adaptive grid archiving (AGA) scheme derived from the Pareto Archived Evolution Strategy (PAES). In contrast, a multi-tiered AGA scheme to populate the archive using an adaptive grid for each level of non-dominated solutions in the considered candidate population is proposed. The new selection scheme improves the performance of the CMA-PAES as shown using benchmark functions from the ZDT, CEC09, and DTLZ test suite in a comparison against the (μ+λ) μ λ Multi-Objective Covariance Matrix Adaptation Evolution Strategy (MO-CMA-ES). In comparison with MO-CMA-ES, the experimental results show that the proposed algorithm offers up to a 69 % performance increase according to the Inverse Generational Distance (IGD) metric