Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance

• URL: http://arxiv.org/abs/2106.06304v2
• Date: Thu, 17 Mar 2022 10:07:52 GMT
• Title: Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance
• Authors: Furong Ye and Carola Doerr and Hao Wang and Thomas B\"ack
• Abstract summary: We show that it might be preferable to use anytime performance measures for the configuration task. tuning for expected running time is much more sensitive with respect to the budget that is allocated to the target algorithms.
• Score: 4.33419118449588
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Finding the best configuration of algorithms' hyperparameters for a given optimization problem is an important task in evolutionary computation. We compare in this work the results of four different hyperparameter tuning approaches for a family of genetic algorithms on 25 diverse pseudo-Boolean optimization problems. More precisely, we compare previously obtained results from a grid search with those obtained from three automated configuration techniques: iterated racing, mixed-integer parallel efficient global optimization, and mixed-integer evolutionary strategies. Using two different cost metrics, expected running time and the area under the empirical cumulative distribution function curve, we find that in several cases the best configurations with respect to expected running time are obtained when using the area under the empirical cumulative distribution function curve as the cost metric during the configuration process. Our results suggest that even when interested in expected running time performance, it might be preferable to use anytime performance measures for the configuration task. We also observe that tuning for expected running time is much more sensitive with respect to the budget that is allocated to the target algorithms.

Related papers

• Performance Evaluation of Evolutionary Algorithms for Analog Integrated Circuit Design Optimisation [0.0]
  An automated sizing approach for analog circuits is presented in this paper. A targeted search of the search space has been implemented using a particle generation function and a repair-bounds function. The algorithms are tuned and modified to converge to a better optimal solution.
  arXiv  Detail & Related papers  (2023-10-19T03:26:36Z)
• Performance Embeddings: A Similarity-based Approach to Automatic Performance Optimization [71.69092462147292]
  Performance embeddings enable knowledge transfer of performance tuning between applications. We demonstrate this transfer tuning approach on case studies in deep neural networks, dense and sparse linear algebra compositions, and numerical weather prediction stencils.
  arXiv  Detail & Related papers  (2023-03-14T15:51:35Z)
• Massively Parallel Genetic Optimization through Asynchronous Propagation of Populations [50.591267188664666]
  Propulate is an evolutionary optimization algorithm and software package for global optimization. We provide an MPI-based implementation of our algorithm, which features variants of selection, mutation, crossover, and migration. We find that Propulate is up to three orders of magnitude faster without sacrificing solution accuracy.
  arXiv  Detail & Related papers  (2023-01-20T18:17:34Z)
• Efficient Non-Parametric Optimizer Search for Diverse Tasks [93.64739408827604]
  We present the first efficient scalable and general framework that can directly search on the tasks of interest. Inspired by the innate tree structure of the underlying math expressions, we re-arrange the spaces into a super-tree. We adopt an adaptation of the Monte Carlo method to tree search, equipped with rejection sampling and equivalent- form detection.
  arXiv  Detail & Related papers  (2022-09-27T17:51:31Z)
• Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces [54.58348769621782]
  Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search. Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function. Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.
  arXiv  Detail & Related papers  (2022-07-02T16:59:37Z)
• Per-run Algorithm Selection with Warm-starting using Trajectory-based Features [5.073358743426584]
  Per-instance algorithm selection seeks to recommend, for a given problem instance, one or several suitable algorithms. We propose an online algorithm selection scheme which we coin per-run algorithm selection. We show that our approach outperforms static per-instance algorithm selection.
  arXiv  Detail & Related papers  (2022-04-20T14:30:42Z)
• Continuation Path with Linear Convergence Rate [18.405645120971496]
  Path-following algorithms are frequently used in composite optimization problems where a series of subproblems are solved sequentially. We present a primal dual analysis of the path-following algorithm as well as determining how accurately each subproblem should be solved to guarantee a linear convergence rate on a target problem. Considering optimization with a sparsity-inducing penalty, we analyze the change of the active sets with respect to the regularization parameter.
  arXiv  Detail & Related papers  (2021-12-09T18:42:13Z)
• Online hyperparameter optimization by real-time recurrent learning [57.01871583756586]
  Our framework takes advantage of the analogy between hyperparameter optimization and parameter learning in neural networks (RNNs) It adapts a well-studied family of online learning algorithms for RNNs to tune hyperparameters and network parameters simultaneously. This procedure yields systematically better generalization performance compared to standard methods, at a fraction of wallclock time.
  arXiv  Detail & Related papers  (2021-02-15T19:36:18Z)
• Optimizing Optimizers: Regret-optimal gradient descent algorithms [9.89901717499058]
  We study the existence, uniqueness and consistency of regret-optimal algorithms. By providing first-order optimality conditions for the control problem, we show that regret-optimal algorithms must satisfy a specific structure in their dynamics. We present fast numerical methods for approximating them, generating optimization algorithms which directly optimize their long-term regret.
  arXiv  Detail & Related papers  (2020-12-31T19:13:53Z)
• Analysis of the Performance of Algorithm Configurators for Search Heuristics with Global Mutation Operators [0.0]
  ParamRLS can efficiently identify the optimal neighbourhood size to be used by local search. We show that the simple ParamRLS-F 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.
  arXiv  Detail & Related papers  (2020-04-09T12:42:30Z)
• Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization [71.03797261151605]
  Adaptivity is an important yet under-studied property in modern optimization theory. Our algorithm is proved to achieve the best-available convergence for non-PL objectives simultaneously while outperforming existing algorithms for PL objectives.
  arXiv  Detail & Related papers  (2020-02-13T05:42:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.