35,147 research outputs found
Discrepancy-based Evolutionary Diversity Optimization
Diversity plays a crucial role in evolutionary computation. While diversity
has been mainly used to prevent the population of an evolutionary algorithm
from premature convergence, the use of evolutionary algorithms to obtain a
diverse set of solutions has gained increasing attention in recent years.
Diversity optimization in terms of features on the underlying problem allows to
obtain a better understanding of possible solutions to the problem at hand and
can be used for algorithm selection when dealing with combinatorial
optimization problems such as the Traveling Salesperson Problem. We explore the
use of the star-discrepancy measure to guide the diversity optimization process
of an evolutionary algorithm.
In our experimental investigations, we consider our discrepancy-based
diversity optimization approaches for evolving diverse sets of images as well
as instances of the Traveling Salesperson problem where a local search is not
able to find near optimal solutions. Our experimental investigations comparing
three diversity optimization approaches show that a discrepancy-based diversity
optimization approach using a tie-breaking rule based on weighted differences
to surrounding feature points provides the best results in terms of the star
discrepancy measure
Optimization Framework and Graph-Based Approach for Relay-Assisted Bidirectional OFDMA Cellular Networks
This paper considers a relay-assisted bidirectional cellular network where
the base station (BS) communicates with each mobile station (MS) using OFDMA
for both uplink and downlink. The goal is to improve the overall system
performance by exploring the full potential of the network in various
dimensions including user, subcarrier, relay, and bidirectional traffic. In
this work, we first introduce a novel three-time-slot time-division duplexing
(TDD) transmission protocol. This protocol unifies direct transmission, one-way
relaying and network-coded two-way relaying between the BS and each MS. Using
the proposed three-time-slot TDD protocol, we then propose an optimization
framework for resource allocation to achieve the following gains: cooperative
diversity (via relay selection), network coding gain (via bidirectional
transmission mode selection), and multiuser diversity (via subcarrier
assignment). We formulate the problem as a combinatorial optimization problem,
which is NP-complete. To make it more tractable, we adopt a graph-based
approach. We first establish the equivalence between the original problem and a
maximum weighted clique problem in graph theory. A metaheuristic algorithm
based on any colony optimization (ACO) is then employed to find the solution in
polynomial time. Simulation results demonstrate that the proposed protocol
together with the ACO algorithm significantly enhances the system total
throughput.Comment: 27 pages, 8 figures, 2 table
Experimental library screening demonstrates the successful application of computational protein design to large structural ensembles
The stability, activity, and solubility of a protein sequence are determined by a delicate balance of molecular interactions in a variety of conformational states. Even so, most computational protein design methods model sequences in the context of a single native conformation. Simulations that model the native state as an ensemble have been mostly neglected due to the lack of sufficiently powerful optimization algorithms for multistate design. Here, we have applied our multistate design algorithm to study the potential utility of various forms of input structural data for design. To facilitate a more thorough analysis, we developed new methods for the design and high-throughput stability determination of combinatorial mutation libraries based on protein design calculations. The application of these methods to the core design of a small model system produced many variants with improved thermodynamic stability and showed that multistate design methods can be readily applied to large structural ensembles. We found that exhaustive screening of our designed libraries helped to clarify several sources of simulation error that would have otherwise been difficult to ascertain. Interestingly, the lack of correlation between our simulated and experimentally measured stability values shows clearly that a design procedure need not reproduce experimental data exactly to achieve success. This surprising result suggests potentially fruitful directions for the improvement of computational protein design technology
Open-ended Learning in Symmetric Zero-sum Games
Zero-sum games such as chess and poker are, abstractly, functions that
evaluate pairs of agents, for example labeling them `winner' and `loser'. If
the game is approximately transitive, then self-play generates sequences of
agents of increasing strength. However, nontransitive games, such as
rock-paper-scissors, can exhibit strategic cycles, and there is no longer a
clear objective -- we want agents to increase in strength, but against whom is
unclear. In this paper, we introduce a geometric framework for formulating
agent objectives in zero-sum games, in order to construct adaptive sequences of
objectives that yield open-ended learning. The framework allows us to reason
about population performance in nontransitive games, and enables the
development of a new algorithm (rectified Nash response, PSRO_rN) that uses
game-theoretic niching to construct diverse populations of effective agents,
producing a stronger set of agents than existing algorithms. We apply PSRO_rN
to two highly nontransitive resource allocation games and find that PSRO_rN
consistently outperforms the existing alternatives.Comment: ICML 2019, final versio
Using Differential Evolution for the Graph Coloring
Differential evolution was developed for reliable and versatile function
optimization. It has also become interesting for other domains because of its
ease to use. In this paper, we posed the question of whether differential
evolution can also be used by solving of the combinatorial optimization
problems, and in particular, for the graph coloring problem. Therefore, a
hybrid self-adaptive differential evolution algorithm for graph coloring was
proposed that is comparable with the best heuristics for graph coloring today,
i.e. Tabucol of Hertz and de Werra and the hybrid evolutionary algorithm of
Galinier and Hao. We have focused on the graph 3-coloring. Therefore, the
evolutionary algorithm with method SAW of Eiben et al., which achieved
excellent results for this kind of graphs, was also incorporated into this
study. The extensive experiments show that the differential evolution could
become a competitive tool for the solving of graph coloring problem in the
future
Self-adaptation of Genetic Operators Through Genetic Programming Techniques
Here we propose an evolutionary algorithm that self modifies its operators at
the same time that candidate solutions are evolved. This tackles convergence
and lack of diversity issues, leading to better solutions. Operators are
represented as trees and are evolved using genetic programming (GP) techniques.
The proposed approach is tested with real benchmark functions and an analysis
of operator evolution is provided.Comment: Presented in GECCO 201
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