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Found 11 papers, 3 papers with code
Superior Genetic Algorithms for the Target Set Selection Problem Based on Power-Law Parameter Choices and Simple Greedy Heuristics
Besides providing a superior algorithm for the TSS problem, this work shows that randomized parameter choices and elementary greedy heuristics can give better results than complex algorithms and costly parameter tuning.
A Flexible Evolutionary Algorithm With Dynamic Mutation Rate Archive
We propose a new, flexible approach for dynamically maintaining successful mutation rates in evolutionary algorithms using $k$-bit flip mutations.
Bivariate Estimation-of-Distribution Algorithms Can Find an Exponential Number of Optima
We show that the bivariate EDA mutual-information-maximizing input clustering, without any problem-specific modification, quickly generates a model that behaves very similarly to a theoretically ideal model for EBOM, which samples each of the exponentially many optima with the same maximal probability.
Estimation-of-Distribution Algorithms for Multi-Valued Decision Variables
Roughly speaking, when the variables take $r$ different values, the time for genetic drift to become significant is $r$ times shorter than in the binary case.
Lasting Diversity and Superior Runtime Guarantees for the $(μ+1)$ Genetic Algorithm
From this better understanding of the population diversity, we obtain stronger runtime guarantees, among them the statement that for all $c\ln(n)\le\mu \le n/\log n$, with $c$ a suitable constant, the runtime of the $(\mu+1)$ GA on $\mathrm{Jump}_k$, with $k \ge 3$, is $O(n^{k-1})$.
Run Time Analysis for Random Local Search on Generalized Majority Functions
We prove upper bounds for the expected run time of random local search on this MAJORITY problem for its entire parameter spectrum.
Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration
We extend this benchmark by analyzing optimal control policies that can select the parameters only from a given portfolio of possible values.
The Univariate Marginal Distribution Algorithm Copes Well With Deception and Epistasis
In their recent work, Lehre and Nguyen (FOGA 2019) show that the univariate marginal distribution algorithm (UMDA) needs time exponential in the parent populations size to optimize the DeceptiveLeadingBlocks (DLB) problem.
Theory of Estimation-of-Distribution Algorithms
Estimation-of-distribution algorithms (EDAs) are general metaheuristics used in optimization that represent a more recent alternative to classical approaches like evolutionary algorithms.
First-Hitting Times Under Additive Drift
As corollaries, the same is true for our upper bounds in the case of variable and multiplicative drift.
Escaping Local Optima using Crossover with Emergent or Reinforced Diversity
This proves a sizeable advantage of all variants of the ($\mu$+1) GA compared to (1+1) EA, which requires time $\Theta(n^k)$.
