7,345 research outputs found
CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features
In this paper we propose a crossover operator for evolutionary algorithms
with real values that is based on the statistical theory of population
distributions. The operator is based on the theoretical distribution of the
values of the genes of the best individuals in the population. The proposed
operator takes into account the localization and dispersion features of the
best individuals of the population with the objective that these features would
be inherited by the offspring. Our aim is the optimization of the balance
between exploration and exploitation in the search process. In order to test
the efficiency and robustness of this crossover, we have used a set of
functions to be optimized with regard to different criteria, such as,
multimodality, separability, regularity and epistasis. With this set of
functions we can extract conclusions in function of the problem at hand. We
analyze the results using ANOVA and multiple comparison statistical tests. As
an example of how our crossover can be used to solve artificial intelligence
problems, we have applied the proposed model to the problem of obtaining the
weight of each network in a ensemble of neural networks. The results obtained
are above the performance of standard methods
Bias and variance in continuous EDA
International audienceEstimation of Distribution Algorithms are based on statistical estimates. We show that when combining classical tools from statistics, namely bias/variance decomposition, reweighting and quasi-randomization, we can strongly improve the convergence rate. All modiïŹcations are easy, compliant with most algorithms, and experimentally very eïŹcient in particular in the parallel case (large oïŹsprings)
Sufficient Conditions for Feasibility and Optimality of Real-Time Optimization Schemes - I. Theoretical Foundations
The idea of iterative process optimization based on collected output
measurements, or "real-time optimization" (RTO), has gained much prominence in
recent decades, with many RTO algorithms being proposed, researched, and
developed. While the essential goal of these schemes is to drive the process to
its true optimal conditions without violating any safety-critical, or "hard",
constraints, no generalized, unified approach for guaranteeing this behavior
exists. In this two-part paper, we propose an implementable set of conditions
that can enforce these properties for any RTO algorithm. The first part of the
work is dedicated to the theory behind the sufficient conditions for
feasibility and optimality (SCFO), together with their basic implementation
strategy. RTO algorithms enforcing the SCFO are shown to perform as desired in
several numerical examples - allowing for feasible-side convergence to the
plant optimum where algorithms not enforcing the conditions would fail.Comment: Working paper; supplementary material available at:
http://infoscience.epfl.ch/record/18807
Multi-population methods with adaptive mutation for multi-modal optimization problems
open access journalThis paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity, the multi-population technique can be applied to maintain the diversity in the population and the convergence capacity of GAs. The proposed scheme is the combination of multi-population with adaptive mutation operator, which determines two different mutation probabilities for different sites of the solutions. The probabilities are updated by the fitness and distribution of solutions in the search space during the evolution process. The experimental results demonstrate the performance of the proposed algorithm based on a set of benchmark problems in comparison with relevant algorithms
Sufficient Conditions for Feasibility and Optimality of Real-Time Optimization Schemes - II. Implementation Issues
The idea of iterative process optimization based on collected output
measurements, or "real-time optimization" (RTO), has gained much prominence in
recent decades, with many RTO algorithms being proposed, researched, and
developed. While the essential goal of these schemes is to drive the process to
its true optimal conditions without violating any safety-critical, or "hard",
constraints, no generalized, unified approach for guaranteeing this behavior
exists. In this two-part paper, we propose an implementable set of conditions
that can enforce these properties for any RTO algorithm. This second part
examines the practical side of the sufficient conditions for feasibility and
optimality (SCFO) proposed in the first and focuses on how they may be enforced
in real application, where much of the knowledge required for the conceptual
SCFO is unavailable. Methods for improving convergence speed are also
considered.Comment: 56 pages, 15 figure
Adaptive particle swarm optimization
An adaptive particle swarm optimization (APSO) that features better search efficiency than classical particle swarm optimization (PSO) is presented. More importantly, it can perform a global search over the entire search space with faster convergence speed. The APSO consists of two main steps. First, by evaluating the population distribution and particle fitness, a real-time evolutionary state estimation procedure is performed to identify one of the following four defined evolutionary states, including exploration, exploitation, convergence, and jumping out in each generation. It enables the automatic control of inertia weight, acceleration coefficients, and other algorithmic parameters at run time to improve the search efficiency and convergence speed. Then, an elitist learning strategy is performed when the evolutionary state is classified as convergence state. The strategy will act on the globally best particle to jump out of the likely local optima. The APSO has comprehensively been evaluated on 12 unimodal and multimodal benchmark functions. The effects of parameter adaptation and elitist learning will be studied. Results show that APSO substantially enhances the performance of the PSO paradigm in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability. As APSO introduces two new parameters to the PSO paradigm only, it does not introduce an additional design or implementation complexity
- âŠ