36,521 research outputs found
Uncertainty And Evolutionary Optimization: A Novel Approach
Evolutionary algorithms (EA) have been widely accepted as efficient solvers
for complex real world optimization problems, including engineering
optimization. However, real world optimization problems often involve uncertain
environment including noisy and/or dynamic environments, which pose major
challenges to EA-based optimization. The presence of noise interferes with the
evaluation and the selection process of EA, and thus adversely affects its
performance. In addition, as presence of noise poses challenges to the
evaluation of the fitness function, it may need to be estimated instead of
being evaluated. Several existing approaches attempt to address this problem,
such as introduction of diversity (hyper mutation, random immigrants, special
operators) or incorporation of memory of the past (diploidy, case based
memory). However, these approaches fail to adequately address the problem. In
this paper we propose a Distributed Population Switching Evolutionary Algorithm
(DPSEA) method that addresses optimization of functions with noisy fitness
using a distributed population switching architecture, to simulate a
distributed self-adaptive memory of the solution space. Local regression is
used in the pseudo-populations to estimate the fitness. Successful applications
to benchmark test problems ascertain the proposed method's superior performance
in terms of both robustness and accuracy.Comment: In Proceedings of the The 9th IEEE Conference on Industrial
Electronics and Applications (ICIEA 2014), IEEE Press, pp. 988-983, 201
Racing Multi-Objective Selection Probabilities
In the context of Noisy Multi-Objective Optimization, dealing with
uncertainties requires the decision maker to define some preferences about how
to handle them, through some statistics (e.g., mean, median) to be used to
evaluate the qualities of the solutions, and define the corresponding Pareto
set. Approximating these statistics requires repeated samplings of the
population, drastically increasing the overall computational cost. To tackle
this issue, this paper proposes to directly estimate the probability of each
individual to be selected, using some Hoeffding races to dynamically assign the
estimation budget during the selection step. The proposed racing approach is
validated against static budget approaches with NSGA-II on noisy versions of
the ZDT benchmark functions
Half a billion simulations: evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model
Multi-agent geographical models integrate very large numbers of spatial
interactions. In order to validate those models large amount of computing is
necessary for their simulation and calibration. Here a new data processing
chain including an automated calibration procedure is experimented on a
computational grid using evolutionary algorithms. This is applied for the first
time to a geographical model designed to simulate the evolution of an early
urban settlement system. The method enables us to reduce the computing time and
provides robust results. Using this method, we identify several parameter
settings that minimise three objective functions that quantify how closely the
model results match a reference pattern. As the values of each parameter in
different settings are very close, this estimation considerably reduces the
initial possible domain of variation of the parameters. The model is thus a
useful tool for further multiple applications on empirical historical
situations
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