Search results for "evolutionary computation"
showing 10 items of 113 documents
Scalability of using Restricted Boltzmann Machines for Combinatorial Optimization
2014
Abstract Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Restricted Boltzmann Machines (RBMs) are generative neural networks with these desired properties. We integrate an RBM into an EDA and evaluate the performance of this system in solving combinatorial optimization problems with a single objective. We assess how the number of fitness evaluations and the CPU time scale with problem size and complexity. The results are compared to the Bayesian Optimization Algorithm (BOA), a state-of-the-art multivariate EDA, and the Dependency Tree Algorithm (DTA), which uses a simpler probability model requiring less computati…
Depth-Adapted CNN for RGB-D cameras
2020
Conventional 2D Convolutional Neural Networks (CNN) extract features from an input image by applying linear filters. These filters compute the spatial coherence by weighting the photometric information on a fixed neighborhood without taking into account the geometric information. We tackle the problem of improving the classical RGB CNN methods by using the depth information provided by the RGB-D cameras. State-of-the-art approaches use depth as an additional channel or image (HHA) or pass from 2D CNN to 3D CNN. This paper proposes a novel and generic procedure to articulate both photometric and geometric information in CNN architecture. The depth data is represented as a 2D offset to adapt …
Structural bias in population-based algorithms
2014
Abstract Challenging optimisation problems are abundant in all areas of science and industry. Since the 1950s, scientists have responded to this by developing ever-diversifying families of ‘black box’ optimisation algorithms. The latter are designed to be able to address any optimisation problem, requiring only that the quality of any candidate solution can be calculated via a ‘fitness function’ specific to the problem. For such algorithms to be successful, at least three properties are required: (i) an effective informed sampling strategy, that guides the generation of new candidates on the basis of the fitnesses and locations of previously visited candidates; (ii) mechanisms to ensure eff…
An Interactive Simple Indicator-Based Evolutionary Algorithm (I-SIBEA) for Multiobjective Optimization Problems
2015
This paper presents a new preference based interactive evolutionary algorithm (I-SIBEA) for solving multiobjective optimization problems using weighted hypervolume. Here the decision maker iteratively provides her/his preference information in the form of identifying preferred and/or non-preferred solutions from a set of nondominated solutions. This preference information provided by the decision maker is used to assign weights of the weighted hypervolume calculation to solutions in subsequent generations. In any generation, the weighted hypervolume is calculated and solutions are selected to the next generation based on their contribution to the weighted hypervolume. The algorithm is compa…
Hierarchical Evolutionary Algorithms and Noise Compensation via Adaptation
2007
Hierarchical Evolutionary Algorithms (HEAs) are Nested Algorithms composed by two or more Evolutionary Algorithms having the same fitness but different populations. More specifically, the fitness of a Higher Level Evolutionary Algorithm (HLEA) is the optimal fitness value returned by a Lower Level Evolutionary Algorithm (LLEA). Due to their algorithmic formulation, the HEAs can be efficiently implemented in Min-Max problems. In this chapter the application of the HEAs is shown for two different Min-Max problems in the field of Structural Optimization. These two problems are the optimal design of an electrical grounding grid and an elastic structure. Since the fitness of a HLEA is given by a…
A novel framework for MR image segmentation and quantification by using MedGA
2019
BACKGROUND AND OBJECTIVES: Image segmentation represents one of the most challenging issues in medical image analysis to distinguish among different adjacent tissues in a body part. In this context, appropriate image pre-processing tools can improve the result accuracy achieved by computer-assisted segmentation methods. Taking into consideration images with a bimodal intensity distribution, image binarization can be used to classify the input pictorial data into two classes, given a threshold intensity value. Unfortunately, adaptive thresholding techniques for two-class segmentation work properly only for images characterized by bimodal histograms. We aim at overcoming these limitations and…
"Table 35" of "Centrality dependence of Pi, K, p production in Pb-Pb collisions at sqrt(sNN) = 2.76 TeV"
2018
PBAR/P ratio in Pb-Pb collisions at sqrt(sNN) = 2.76 TeV.
"Table 33" of "Centrality dependence of Pi, K, p production in Pb-Pb collisions at sqrt(sNN) = 2.76 TeV"
2018
pi-/pi+ ratio in Pb-Pb collisions at sqrt(sNN) = 2.76 TeV.
"Table 34" of "Centrality dependence of Pi, K, p production in Pb-Pb collisions at sqrt(sNN) = 2.76 TeV"
2018
K-/K+ ratio in Pb-Pb collisions at sqrt(sNN) = 2.76 TeV.
An ant colony optimization-based fuzzy predictive control approach for nonlinear processes
2015
In this paper, a new approach for designing an adaptive fuzzy model predictive control (AFMPC) based on the ant colony optimization (ACO) is proposed. On-line adaptive fuzzy identification is introduced to identify the system parameters. These parameters are used to calculate the objective function based on a predictive approach and structure of RST control. Then the optimization problem is solved based on an ACO algorithm, used at the optimization process in AFMPC to determine optimal controller parameters of RST control. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, where the proposed approach provides better performances compared with p…