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Found 30 papers, 1 papers with code
Theoretical Analysis of Quality Diversity Algorithms for a Classical Path Planning Problem
Quality diversity (QD) algorithms have shown to provide sets of high quality solutions for challenging problems in robotics, games, and combinatorial optimisation.
Evolutionary Algorithms for One-Sided Bipartite Crossing Minimisation
Evolutionary algorithms (EAs) are universal solvers inspired by principles of natural evolution.
Illustrating the Efficiency of Popular Evolutionary Multi-Objective Algorithms Using Runtime Analysis
Runtime analysis has recently been applied to popular evolutionary multi-objective (EMO) algorithms like NSGA-II in order to establish a rigorous theoretical foundation.
Runtime Analyses of NSGA-III on Many-Objective Problems
NSGA-II and NSGA-III are two of the most popular evolutionary multi-objective algorithms used in practice.
A Tight $O(4^k/p_c)$ Runtime Bound for a ($μ$+1) GA on Jump$_k$ for Realistic Crossover Probabilities
This yields an improved and tight time bound of $O(\mu n \log(k) + 4^k/p_c)$ for a range of~$k$ under the mild assumptions $p_c = O(1/k)$ and $\mu \in \Omega(kn)$.
Analysing the Robustness of NSGA-II under Noise
To our knowledge, this is the first proof that NSGA-II can outperform GSEMO and the first runtime analysis of NSGA-II in noisy optimisation.
Runtime Analysis of Quality Diversity Algorithms
Quality diversity~(QD) is a branch of evolutionary computation that gained increasing interest in recent years.
Comma Selection Outperforms Plus Selection on OneMax with Randomly Planted Optima
For certain parameters, the $(1,\lambda)$ EA finds the target in $\Theta(n \ln n)$ evaluations, with high probability (w. h. p.
Analysing Equilibrium States for Population Diversity
We give an exact formula for the drift of population diversity and show that it is driven towards an equilibrium state.
Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation
We provide a theoretical analysis of the well-known EMO algorithms GSEMO and NSGA-II to showcase the possible advantages of crossover: we propose classes of "royal road" functions on which these algorithms cover the whole Pareto front in expected polynomial time if crossover is being used.
Hard Problems are Easier for Success-based Parameter Control
Recent works showed that simple success-based rules for self-adjusting parameters in evolutionary algorithms (EAs) can match or outperform the best fixed parameters on discrete problems.
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.
Runtime Analysis of Restricted Tournament Selection for Bimodal Optimisation
We prove that RTS finds both optima on ${\rm T{\small WO}M{\small AX}}$ efficiently if the window size $w$ is large enough.
Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem
In most settings the expected reoptimization time for such tailored algorithms is linear in the number of added edges.
Self-Adjusting Population Sizes for Non-Elitist Evolutionary Algorithms: Why Success Rates Matter
However, the majority of these studies concerned elitist EAs and we do not have a clear answer on whether the same mechanisms can be applied for non-elitist EAs.
Fast Perturbative Algorithm Configurators
Recent work has shown that the ParamRLS and ParamILS algorithm configurators can tune some simple randomised search heuristics for standard benchmark functions in linear expected time in the size of the parameter space.
More Effective Randomized Search Heuristics for Graph Coloring Through Dynamic Optimization
We show that EAs can solve the graph coloring problem for bipartite graphs more efficiently by using dynamic optimization.
Analysis of the Performance of Algorithm Configurators for Search Heuristics with Global Mutation Operators
To show this we prove that the simple ParamRLS-F configurator can identify the optimal mutation rates even when using cutoff times that are considerably smaller than the expected optimisation time of the best parameter value for both problem classes.
On the Impact of the Cutoff Time on the Performance of Algorithm Configurators
We evaluate the performance of a simple random local search configurator (ParamRLS) for tuning the neighbourhood size $k$ of the RLS$_k$ algorithm.
Parallel Black-Box Complexity with Tail Bounds
Our main result is a general performance limit: we prove that on every function every $\lambda$-parallel unary unbiased algorithm needs at least $\Omega(\frac{\lambda n}{\ln \lambda} + n \log n)$ evaluations to find any desired target set of up to exponential size, with an overwhelming probability.
Analysing the Robustness of Evolutionary Algorithms to Noise: Refined Runtime Bounds and an Example Where Noise is Beneficial
We analyse the performance of well-known evolutionary algorithms (1+1)EA and (1+$\lambda$)EA in the prior noise model, where in each fitness evaluation the search point is altered before evaluation with probability $p$.
Design and Analysis of Diversity-Based Parent Selection Schemes for Speeding Up Evolutionary Multi-objective Optimisation
We show that stagnation might occur when favouring individuals with a high diversity contribution in the parent selection step and provide a discussion on which scheme to use for more complex problems based on our theoretical and experimental results.
Memetic Algorithms Beat Evolutionary Algorithms on the Class of Hurdle Problems
Memetic algorithms are popular hybrid search heuristics that integrate local search into the search process of an evolutionary algorithm in order to combine the advantages of rapid exploitation and global optimisation.
Runtime Analysis of Probabilistic Crowding and Restricted Tournament Selection for Bimodal Optimisation
On Twomax probabilistic crowding fails to find any reasonable solution quality even in exponential time.
On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism
Clearing is a niching method inspired by the principle of assigning the available resources among a niche to a single individual.
The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses
These works show that the benefits of diversity are manifold: diversity is important for global exploration and the ability to find several global optima.
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 $\Theta(n^k)$.
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.
First Steps Towards a Runtime Comparison of Natural and Artificial Evolution
Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired by natural evolution.
How Crossover Speeds Up Building-Block Assembly in Genetic Algorithms
We re-investigate a fundamental question: how effective is crossover in Genetic Algorithms in combining building blocks of good solutions?
