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Found 14 papers, 0 papers with code
Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem
The diversity optimization is the class of optimization problems, in which we aim at finding a diverse set of good solutions.
Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem
We introduce a 3-objective formulation that is able to deal with the stochastic and dynamic components at the same time and is independent of the confidence level required for the constraint.
Already Moderate Population Sizes Provably Yield Strong Robustness to Noise
The only previous result in this direction regarded the less realistic one-bit noise model, required a population size super-linear in the problem size, and proved a runtime guarantee roughly cubic in the noiseless runtime for the OneMax benchmark.
Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax
The evolutionary diversity optimization aims at finding a diverse set of solutions which satisfy some constraint on their fitness.
Larger Offspring Populations Help the $(1 + (λ, λ))$ Genetic Algorithm to Overcome the Noise
Evolutionary algorithms are known to be robust to noise in the evaluation of the fitness.
Coevolutionary Pareto Diversity Optimization
Our new Pareto Diversity optimization approach uses this bi-objective formulation to optimize the problem while also maintaining an additional population of high quality solutions for which diversity is optimized with respect to a given diversity measure.
Lazy Parameter Tuning and Control: Choosing All Parameters Randomly From a Power-Law Distribution
On the other hand, this algorithm is also very efficient on jump functions, where the best static parameters are very different from those necessary to optimize simple problems.
First Steps Towards a Runtime Analysis When Starting With a Good Solution
The mathematical runtime analysis of evolutionary algorithms traditionally regards the time an algorithm needs to find a solution of a certain quality when initialized with a random population.
Runtime Analysis of a Heavy-Tailed $(1+(λ,λ))$ Genetic Algorithm on Jump Functions
To obtain this performance, however, a non-standard parameter setting depending on the jump size $k$ was used.
Fast Mutation in Crossover-based Algorithms
In this first runtime analysis of a crossover-based algorithm using a heavy-tailed choice of the mutation rate, we show an even stronger impact.
A Rigorous Runtime Analysis of the $(1 + (λ, λ))$ GA on Jump Functions
In this work, we conduct the first runtime analysis of this algorithm on a multimodal problem class, the jump functions benchmark.
The Efficiency Threshold for the Offspring Population Size of the ($μ$, $λ$) EA
Understanding when evolutionary algorithms are efficient or not, and how they efficiently solve problems, is one of the central research tasks in evolutionary computation.
A Tight Runtime Analysis for the $(μ+ λ)$ EA
In this work, we analyze this long-standing problem and show the asymptotically tight result that the runtime $T$, the number of iterations until the optimum is found, satisfies \[E[T] = \Theta\bigg(\frac{n\log n}{\lambda}+\frac{n}{\lambda / \mu} + \frac{n\log^+\log^+ \lambda/ \mu}{\log^+ \lambda / \mu}\bigg),\] where $\log^+ x := \max\{1, \log x\}$ for all $x > 0$.
Precise Runtime Analysis for Plateau Functions
To gain a better theoretical understanding of how evolutionary algorithms (EAs) cope with plateaus of constant fitness, we propose the $n$-dimensional Plateau$_k$ function as natural benchmark and analyze how different variants of the $(1 + 1)$ EA optimize it.
