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Found 26 papers, 2 papers with code
Runtime Analysis of a Multi-Valued Compact Genetic Algorithm on Generalized OneMax
A class of metaheuristic techniques called estimation-of-distribution algorithms (EDAs) are employed in optimization as more sophisticated substitutes for traditional strategies like evolutionary algorithms.
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.
First Steps Towards a Runtime Analysis of Neuroevolution
We consider a simple setting in neuroevolution where an evolutionary algorithm optimizes the weights and activation functions of a simple artificial neural network.
Fast Pareto Optimization Using Sliding Window Selection
Pareto optimization using evolutionary multi-objective algorithms has been widely applied to solve constrained submodular optimization problems.
How Well Does the Metropolis Algorithm Cope With Local Optima?
Even with the optimal temperature (the only parameter of the MA), the MA optimizes most cliff functions less efficiently than simple elitist evolutionary algorithms (EAs), which can only leave the local optimum by generating a superior solution possibly far away.
3-Objective Pareto Optimization for Problems with Chance Constraints
Evolutionary multi-objective algorithms have successfully been used in the context of Pareto optimization where a given constraint is relaxed into an additional objective.
Runtime Analysis of the (1+1) EA on Weighted Sums of Transformed Linear Functions
Linear functions play a key role in the runtime analysis of evolutionary algorithms and studies have provided a wide range of new insights and techniques for analyzing evolutionary computation methods.
The Compact Genetic Algorithm Struggles on Cliff Functions
We point out that the cGA faces major difficulties when solving the CLIFF function and investigate its dynamics both experimentally and theoretically around the cliff.
Simulated Annealing is a Polynomial-Time Approximation Scheme for the Minimum Spanning Tree Problem
We prove that Simulated Annealing with an appropriate cooling schedule computes arbitrarily tight constant-factor approximations to the minimum spanning tree problem in polynomial time.
Runtime Analysis of Single- and Multi-Objective Evolutionary Algorithms for Chance Constrained Optimization Problems with Normally Distributed Random Variables
With this paper, we contribute to the theoretical understanding of evolutionary algorithms for chance constrained optimization.
Stagnation Detection in Highly Multimodal Fitness Landscapes
In multimodal landscapes with a more complex location of optima of similar gap size, stagnation detection suffers from the fact that the neighborhood size is frequently reset to $1$ without using gap sizes that were promising in the past.
On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials is Best
On the other hand, we prove that selecting the worst individual as parent leads to efficient global optimisation with overwhelming probability for reasonable population sizes.
Stagnation Detection with Randomized Local Search
The so-called $SD-(1+1)EA$ introduced by Rajabi and Witt (GECCO 2020) adds stagnation detection to the classical $(1+1)EA$ with standard bit mutation, which flips each bit independently with some mutation rate, and raises the mutation rate when the algorithm is likely to have encountered local optima.
Improved Runtime Results for Simple Randomised Search Heuristics on Linear Functions with a Uniform Constraint
Linear functions have been traditionally studied in this area resulting in tight bounds on the expected optimisation time of simple randomised search algorithms for this class of problems.
Evolutionary Algorithms with Self-adjusting Asymmetric Mutation
Evolutionary Algorithms (EAs) and other randomized search heuristics are often considered as unbiased algorithms that are invariant with respect to different transformations of the underlying search space.
Improved Fixed-Budget Results via Drift Analysis
Fixed-budget theory is concerned with computing or bounding the fitness value achievable by randomized search heuristics within a given budget of fitness function evaluations.
Self-Adjusting Evolutionary Algorithms for Multimodal Optimization
We suggest a mechanism called stagnation detection that can be added as a module to existing evolutionary algorithms (both with and without prior self-adjusting algorithms).
Sharp Bounds on the Runtime of the (1+1) EA via Drift Analysis and Analytic Combinatorial Tools
This paper revisits drift analysis for the (1+1) EA on OneMax and obtains that the expected running time $E(T)$, starting from $\lceil n/2\rceil$ one-bits, is determined by the sum of inverse drifts up to logarithmic error terms, more precisely $$\sum_{k=1}^{\lfloor n/2\rfloor}\frac{1}{\Delta(k)} - c_1\log n \le E(T) \le \sum_{k=1}^{\lfloor n/2\rfloor}\frac{1}{\Delta(k)} - c_2\log n,$$ where $\Delta(k)$ is the drift (expected increase of the number of one-bits from the state of $n-k$ ones) and $c_1, c_2 >0$ are explicitly computed constants.
Runtime Analysis for Self-adaptive Mutation Rates
We propose and analyze a self-adaptive version of the $(1,\lambda)$ evolutionary algorithm in which the current mutation rate is part of the individual and thus also subject to mutation.
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.
The (1+$λ$) Evolutionary Algorithm with Self-Adjusting Mutation Rate
We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms in discrete search spaces.
Upper Bounds on the Runtime of the Univariate Marginal Distribution Algorithm on OneMax
If $\mu\ge c\log n$ for some constant $c>0$ and $\lambda=(1+\Theta(1))\mu$, a general bound $O(\mu n)$ on the expected runtime is obtained.
Update Strength in EDAs and ACO: How to Avoid Genetic Drift
We provide a rigorous runtime analysis concerning the update strength, a vital parameter in probabilistic model-building GAs such as the step size $1/K$ in the compact Genetic Algorithm (cGA) and the evaporation factor $\rho$ in ACO.
On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling
Evolutionary algorithms have been frequently used for dynamic optimization problems.
The Fitness Level Method with Tail Bounds
The fitness-level method, also called the method of f-based partitions, is an intuitive and widely used technique for the running time analysis of randomized search heuristics.
General Drift Analysis with Tail Bounds
We address this lack by providing a general drift theorem that includes bounds on the upper and lower tail of the hitting time distribution.
