26,291 research outputs found
An experimental comparative study for interactive evolutionary computation problems
Proceeding of: EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC, Budapest, Hungary, April 10-12, 2006This paper presents an objective experimental comparative study between four algorithms: the Genetic Algorithm, the Fitness Prediction Genetic Algorithm, the Population Based Incremental Learning algorithm and the purposed method based on the Chromosome Appearance Probability Matrix. The comparative is done with a non subjective evaluation function. The main objective is to validate the efficiency of several methods in Interactive Evolutionary Computation environments. The most important constraint of working within those environments is the user interaction, which affects the results adding time restrictions for the experimentation stage and subjectivity to the validation. The experiments done in this paper replace user interaction with several approaches avoiding user limitations. So far, the results show the efficiency of the purposed algorithm in terms of quality of solutions and convergence speed, two known keys to decrease the user fatigue.This article has been financed by the Spanish founded research MCyT project OPLINK, Ref: TIN2006-08818-C04-02
Multi-PBil: an estimation distribution algorithm applied to multimodal optimization problems
The Estimation Distribution Algorithms (EDAs) compose an evolutionary metaheuristic whose main characteristic is the construction of solutions in randomly form, using a distribution of probabilities that evolves during the execution.
The Population-Based Incremental Learning Algorithm (PBIL) is a type of EDA where the variables are independent, that is, they do not have significant interactions between themselves. The PBIL considers that the solutions can be represented as vectors of discrete variables, what makes it more adequate for combinatorial optimization problems. This paper presents a method called Multi-PBil that is an extension of PBIL with applications in multimodal problems. The Multi-PBil was developed with the goal to have an efficient and non expensive algorithm of search in multimodal spaces. From PBIL, it was implemented a routine that allows the Multi-PBil to create a probability model to act in the search space. A formula that allows initiating the probability models in regions of the search space next to the searched global points was applied in the process of the probability model initialization rule. The Multi-PBil method was tested and analyzed, presenting some experimental results that highlight its viability and characteristics. It is also shown a comparison of the performance between the Multi-PBil and a traditional Genetic Algorithm using the sharing method.VII Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI
Population-based continuous optimization, probabilistic modelling and mean shift
Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems
Feature Reinforcement Learning: Part I: Unstructured MDPs
General-purpose, intelligent, learning agents cycle through sequences of
observations, actions, and rewards that are complex, uncertain, unknown, and
non-Markovian. On the other hand, reinforcement learning is well-developed for
small finite state Markov decision processes (MDPs). Up to now, extracting the
right state representations out of bare observations, that is, reducing the
general agent setup to the MDP framework, is an art that involves significant
effort by designers. The primary goal of this work is to automate the reduction
process and thereby significantly expand the scope of many existing
reinforcement learning algorithms and the agents that employ them. Before we
can think of mechanizing this search for suitable MDPs, we need a formal
objective criterion. The main contribution of this article is to develop such a
criterion. I also integrate the various parts into one learning algorithm.
Extensions to more realistic dynamic Bayesian networks are developed in Part
II. The role of POMDPs is also considered there.Comment: 24 LaTeX pages, 5 diagram
Objective Improvement in Information-Geometric Optimization
Information-Geometric Optimization (IGO) is a unified framework of stochastic
algorithms for optimization problems. Given a family of probability
distributions, IGO turns the original optimization problem into a new
maximization problem on the parameter space of the probability distributions.
IGO updates the parameter of the probability distribution along the natural
gradient, taken with respect to the Fisher metric on the parameter manifold,
aiming at maximizing an adaptive transform of the objective function. IGO
recovers several known algorithms as particular instances: for the family of
Bernoulli distributions IGO recovers PBIL, for the family of Gaussian
distributions the pure rank-mu CMA-ES update is recovered, and for exponential
families in expectation parametrization the cross-entropy/ML method is
recovered. This article provides a theoretical justification for the IGO
framework, by proving that any step size not greater than 1 guarantees monotone
improvement over the course of optimization, in terms of q-quantile values of
the objective function f. The range of admissible step sizes is independent of
f and its domain. We extend the result to cover the case of different step
sizes for blocks of the parameters in the IGO algorithm. Moreover, we prove
that expected fitness improves over time when fitness-proportional selection is
applied, in which case the RPP algorithm is recovered
An ADMM Based Framework for AutoML Pipeline Configuration
We study the AutoML problem of automatically configuring machine learning
pipelines by jointly selecting algorithms and their appropriate
hyper-parameters for all steps in supervised learning pipelines. This black-box
(gradient-free) optimization with mixed integer & continuous variables is a
challenging problem. We propose a novel AutoML scheme by leveraging the
alternating direction method of multipliers (ADMM). The proposed framework is
able to (i) decompose the optimization problem into easier sub-problems that
have a reduced number of variables and circumvent the challenge of mixed
variable categories, and (ii) incorporate black-box constraints along-side the
black-box optimization objective. We empirically evaluate the flexibility (in
utilizing existing AutoML techniques), effectiveness (against open source
AutoML toolkits),and unique capability (of executing AutoML with practically
motivated black-box constraints) of our proposed scheme on a collection of
binary classification data sets from UCI ML& OpenML repositories. We observe
that on an average our framework provides significant gains in comparison to
other AutoML frameworks (Auto-sklearn & TPOT), highlighting the practical
advantages of this framework
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