40,154 research outputs found
Global Impact Balancing in the Hierarchic Genetic Search
The new Globally Balanced Hierarchic Genetic Strategy (GB-HGS) was introduced as a tool for solving difficult global optimization problems. This strategy provides a multi-deme economic stochastic search with an adaptive accuracy that allows many local extremes of the objective to be found. The strategy was designed according to the Multi Agent System (MAS) paradigm. The novelty of GB-HGS derives from its control of the search impact performed by various demes on the basis of the global information gathered and exchanged among the computing agents. This mechanism is applied together with the local profiling of the computational process already used in the previous versions of hierarchic genetic computations. The new strategy exhibits better efficiency, especially in the second phase of computations, when the promising regions containing the global extremes are encountered
Discovering Evolutionary Stepping Stones through Behavior Domination
Behavior domination is proposed as a tool for understanding and harnessing
the power of evolutionary systems to discover and exploit useful stepping
stones. Novelty search has shown promise in overcoming deception by collecting
diverse stepping stones, and several algorithms have been proposed that combine
novelty with a more traditional fitness measure to refocus search and help
novelty search scale to more complex domains. However, combinations of novelty
and fitness do not necessarily preserve the stepping stone discovery that
novelty search affords. In several existing methods, competition between
solutions can lead to an unintended loss of diversity. Behavior domination
defines a class of algorithms that avoid this problem, while inheriting
theoretical guarantees from multiobjective optimization. Several existing
algorithms are shown to be in this class, and a new algorithm is introduced
based on fast non-dominated sorting. Experimental results show that this
algorithm outperforms existing approaches in domains that contain useful
stepping stones, and its advantage is sustained with scale. The conclusion is
that behavior domination can help illuminate the complex dynamics of
behavior-driven search, and can thus lead to the design of more scalable and
robust algorithms.Comment: To Appear in Proceedings of the Genetic and Evolutionary Computation
Conference (GECCO 2017
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Finding High-Dimensional D-OptimalDesigns for Logistic Models via Differential Evolution
D-optimal designs are frequently used in controlled experiments to obtain the most accurateestimate of model parameters at minimal cost. Finding them can be a challenging task, especially whenthere are many factors in a nonlinear model. As the number of factors becomes large and interact withone another, there are many more variables to optimize and the D-optimal design problem becomes highdimensionaland non-separable. Consequently, premature convergence issues arise. Candidate solutions gettrapped in local optima and the classical gradient-based optimization approaches to search for the D-optimaldesigns rarely succeed. We propose a specially designed version of differential evolution (DE) which is arepresentative gradient-free optimization approach to solve such high-dimensional optimization problems.The proposed specially designed DE uses a new novelty-based mutation strategy to explore the variousregions in the search space. The exploration of the regions will be carried out differently from the previouslyexplored regions and the diversity of the population can be preserved. The proposed novelty-based mutationstrategy is collaborated with two common DE mutation strategies to balance exploration and exploitationat the early or medium stage of the evolution. Additionally, we adapt the control parameters of DE as theevolution proceeds. Using logistic models with several factors on various design spaces as examples, oursimulation results show our algorithm can find D-optimal designs efficiently and the algorithm outperformsits competitors. As an application, we apply our algorithm and re-design a 10-factor car refueling experimentwith discrete and continuous factors and selected pairwise interactions. Our proposed algorithm was able toconsistently outperform the other algorithms and find a more efficient D-optimal design for the problem
Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations
The large number of possible configurations of modern software-based systems,
combined with the large number of possible environmental situations of such
systems, prohibits enumerating all adaptation options at design time and
necessitates planning at run time to dynamically identify an appropriate
configuration for a situation. While numerous planning techniques exist, they
typically assume a detailed state-based model of the system and that the
situations that warrant adaptations are known. Both of these assumptions can be
violated in complex, real-world systems. As a result, adaptation planning must
rely on simple models that capture what can be changed (input parameters) and
observed in the system and environment (output and context parameters). We
therefore propose planning as optimization: the use of optimization strategies
to discover optimal system configurations at runtime for each distinct
situation that is also dynamically identified at runtime. We apply our approach
to CrowdNav, an open-source traffic routing system with the characteristics of
a real-world system. We identify situations via clustering and conduct an
empirical study that compares Bayesian optimization and two types of
evolutionary optimization (NSGA-II and novelty search) in CrowdNav
Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems
Majority of Artificial Neural Network (ANN) implementations in autonomous
systems use a fixed/user-prescribed network topology, leading to sub-optimal
performance and low portability. The existing neuro-evolution of augmenting
topology or NEAT paradigm offers a powerful alternative by allowing the network
topology and the connection weights to be simultaneously optimized through an
evolutionary process. However, most NEAT implementations allow the
consideration of only a single objective. There also persists the question of
how to tractably introduce topological diversification that mitigates
overfitting to training scenarios. To address these gaps, this paper develops a
multi-objective neuro-evolution algorithm. While adopting the basic elements of
NEAT, important modifications are made to the selection, speciation, and
mutation processes. With the backdrop of small-robot path-planning
applications, an experience-gain criterion is derived to encapsulate the amount
of diverse local environment encountered by the system. This criterion
facilitates the evolution of genes that support exploration, thereby seeking to
generalize from a smaller set of mission scenarios than possible with
performance maximization alone. The effectiveness of the single-objective
(optimizing performance) and the multi-objective (optimizing performance and
experience-gain) neuro-evolution approaches are evaluated on two different
small-robot cases, with ANNs obtained by the multi-objective optimization
observed to provide superior performance in unseen scenarios
Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation
Unlike unsupervised approaches such as autoencoders that learn to reconstruct
their inputs, this paper introduces an alternative approach to unsupervised
feature learning called divergent discriminative feature accumulation (DDFA)
that instead continually accumulates features that make novel discriminations
among the training set. Thus DDFA features are inherently discriminative from
the start even though they are trained without knowledge of the ultimate
classification problem. Interestingly, DDFA also continues to add new features
indefinitely (so it does not depend on a hidden layer size), is not based on
minimizing error, and is inherently divergent instead of convergent, thereby
providing a unique direction of research for unsupervised feature learning. In
this paper the quality of its learned features is demonstrated on the MNIST
dataset, where its performance confirms that indeed DDFA is a viable technique
for learning useful features.Comment: Corrected citation formattin
Optimization as a design strategy. Considerations based on building simulation-assisted experiments about problem decomposition
In this article the most fundamental decomposition-based optimization method
- block coordinate search, based on the sequential decomposition of problems in
subproblems - and building performance simulation programs are used to reason
about a building design process at micro-urban scale and strategies are defined
to make the search more efficient. Cyclic overlapping block coordinate search
is here considered in its double nature of optimization method and surrogate
model (and metaphore) of a sequential design process. Heuristic indicators apt
to support the design of search structures suited to that method are developed
from building-simulation-assisted computational experiments, aimed to choose
the form and position of a small building in a plot. Those indicators link the
sharing of structure between subspaces ("commonality") to recursive
recombination, measured as freshness of the search wake and novelty of the
search moves. The aim of these indicators is to measure the relative
effectiveness of decomposition-based design moves and create efficient block
searches. Implications of a possible use of these indicators in genetic
algorithms are also highlighted.Comment: 48 pages. 12 figures, 3 table
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