Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling $(1+\lambda)$ EA Variants on OneMax and LeadingOnes
Reads0
Chats0
TLDR
In this paper, the authors adjust the COCO software to pseudo-Boolean optimization problems, and obtain from this a benchmarking environment that allows a fine-grained empirical analysis of discrete black-box heuristics.Abstract:
Theoretical and empirical research on evolutionary computation methods complement each other by providing two fundamentally different approaches towards a better understanding of black-box optimization heuristics. In discrete optimization, both streams developed rather independently of each other, but we observe today an increasing interest in reconciling these two sub-branches. In continuous optimization, the COCO (COmparing Continuous Optimisers) benchmarking suite has established itself as an important platform that theoreticians and practitioners use to exchange research ideas and questions. No widely accepted equivalent exists in the research domain of discrete black-box optimization.
Marking an important step towards filling this gap, we adjust the COCO software to pseudo-Boolean optimization problems, and obtain from this a benchmarking environment that allows a fine-grained empirical analysis of discrete black-box heuristics. In this documentation we demonstrate how this test bed can be used to profile the performance of evolutionary algorithms. More concretely, we study the optimization behavior of several $(1+\lambda)$ EA variants on the two benchmark problems OneMax and LeadingOnes. This comparison motivates a refined analysis for the optimization time of the $(1+\lambda)$ EA on LeadingOnes.read more
Citations
More filters
Proceedings ArticleDOI
Self-adjusting evolutionary algorithms for multimodal optimization
Amirhossein Rajabi,Carsten Witt +1 more
TL;DR: This work suggests a mechanism called stagnation detection that can be added as a module to existing evolutionary algorithms (both with and without prior self-adjusting schemes), and proves an expected runtime on the well-known Jump benchmark that corresponds to an asymptotically optimal parameter setting and outperforms other mechanisms for multimodal optimization like heavy-tailed mutation.
Journal ArticleDOI
Benchmarking discrete optimization heuristics with IOHprofiler
TL;DR: This work compiles and assess a selection of 23 discrete optimization problems that subscribe to different types of fitness landscapes, and provides a new module for IOHprofiler which extents the fixed-target and fixed-budget results for the individual problems by ECDF results, which allows one to derive aggregated performance statistics for groups of problems.
Journal ArticleDOI
Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design
Andrea Tangherloni,Simone Spolaor,Paolo Cazzaniga,Daniela Besozzi,Leonardo Rundo,Giancarlo Mauri,Marco S. Nobile +6 more
TL;DR: It is state that classic benchmark functions cannot be fully representative of all the features that make real-world optimization problems hard to solve, and this is the case, in particular, of the PE of biochemical systems.
Proceedings ArticleDOI
Benchmarking discrete optimization heuristics with IOHprofiler
TL;DR: This work compiles and assesses a selection of discrete optimization problems that subscribe to different types of fitness landscapes, and compares performances of eleven different heuristics for each selected problem.
Journal ArticleDOI
Salp Swarm Optimization: A critical review
TL;DR: In this paper , a mathematically correct version of SSO, named Amended Salp Swarm Optimizer (ASSO), was proposed to solve all the discussed problems of the original SSO.
References
More filters
Journal ArticleDOI
Parameter Control in Evolutionary Algorithms: Trends and Challenges
TL;DR: More than a decade after the first extensive overview on parameter control, this work revisits the field and presents a survey of the state-of-the-art.
Journal ArticleDOI
COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
TL;DR: COCO as discussed by the authors is an open source platform for comparing continuous optimizers in a black-box setting, which aims at automatizing the tedious and repetitive task of benchmarking numerical optimization algorithms to the greatest possible extent.
Journal ArticleDOI
On the Choice of the Offspring Population Size in Evolutionary Algorithms
TL;DR: Using a simplified but still realistic evolutionary algorithm, a thorough analysis of the effects of the offspring population size is presented and a simple way to dynamically adapt this parameter when necessary is suggested.
Journal ArticleDOI
Tight Bounds on the Optimization Time of a Randomized Search Heuristic on Linear Functions
TL;DR: The standard mutation probability p = 1/n is optimal for all linear functions, and the (1+1) EA is found to be an optimal mutation-based algorithm that turns out to be surprisingly robust since the large neighbourhood explored by the mutation operator does not disrupt the search.
Book ChapterDOI
Optimal fixed and adaptive mutation rates for the leadingones problem
TL;DR: This work reconsiders a classical problem, namely how the (1+1) evolutionary algorithm optimizes the LEADINGONES function, and proves that if a mutation probability of p is used and the problem size is n, then the optimization time is 1/2p2 ((1 - p)-n+1 - (1- p)).
Related Papers (5)
From black-box complexity to designing new genetic algorithms
Optimal Static and Self-Adjusting Parameter Choices for the $$(1+(\lambda ,\lambda ))$$ Genetic Algorithm
Benjamin Doerr,Carola Doerr +1 more