Theoretical Analysis of Quality Diversity Algorithms for a Classical Path Planning Problem
- URL: http://arxiv.org/abs/2412.11446v1
- Date: Mon, 16 Dec 2024 04:58:32 GMT
- Title: Theoretical Analysis of Quality Diversity Algorithms for a Classical Path Planning Problem
- Authors: Duc-Cuong Dang, Aneta Neumann, Frank Neumann, Andre Opris, Dirk Sudholt,
- Abstract summary: We study the behaviour of Quality diversity (QD) algorithms for a classical planning problem seeking several solutions.
Our results show that Map-Elites QD algorithms are able to compute a shortest path for each pair of nodes efficiently in parallel.
- Score: 12.1622929638257
- License:
- Abstract: Quality diversity (QD) algorithms have shown to provide sets of high quality solutions for challenging problems in robotics, games, and combinatorial optimisation. So far, theoretical foundational explaining their good behaviour in practice lack far behind their practical success. We contribute to the theoretical understanding of these algorithms and study the behaviour of QD algorithms for a classical planning problem seeking several solutions. We study the all-pairs-shortest-paths (APSP) problem which gives a natural formulation of the behavioural space based on all pairs of nodes of the given input graph that can be used by Map-Elites QD algorithms. Our results show that Map-Elites QD algorithms are able to compute a shortest path for each pair of nodes efficiently in parallel. Furthermore, we examine parent selection techniques for crossover that exhibit significant speed ups compared to the standard QD approach.
Related papers
- Quality-Diversity Algorithms Can Provably Be Helpful for Optimization [24.694984679399315]
Quality-Diversity (QD) algorithms aim to find a set of high-performing, yet diverse solutions.
This paper tries to shed some light on the optimization ability of QD algorithms via rigorous running time analysis.
arXiv Detail & Related papers (2024-01-19T07:40:24Z) - Accelerating Cutting-Plane Algorithms via Reinforcement Learning
Surrogates [49.84541884653309]
A current standard approach to solving convex discrete optimization problems is the use of cutting-plane algorithms.
Despite the existence of a number of general-purpose cut-generating algorithms, large-scale discrete optimization problems continue to suffer from intractability.
We propose a method for accelerating cutting-plane algorithms via reinforcement learning.
arXiv Detail & Related papers (2023-07-17T20:11:56Z) - A Review on Quantum Approximate Optimization Algorithm and its Variants [47.89542334125886]
The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising variational quantum algorithm that aims to solve intractable optimization problems.
This comprehensive review offers an overview of the current state of QAOA, encompassing its performance analysis in diverse scenarios.
We conduct a comparative study of selected QAOA extensions and variants, while exploring future prospects and directions for the algorithm.
arXiv Detail & Related papers (2023-06-15T15:28:12Z) - Stochastic Ratios Tracking Algorithm for Large Scale Machine Learning
Problems [0.7614628596146599]
We propose a novel algorithm for adaptive step length selection in the classical SGD framework.
Under reasonable conditions, the algorithm produces step lengths in line with well-established theoretical requirements.
We show that the algorithm can generate step lengths comparable to the best step length obtained from manual tuning.
arXiv Detail & Related papers (2023-05-17T06:22:11Z) - Analysis of Quality Diversity Algorithms for the Knapsack Problem [14.12876643502492]
We apply the QD paradigm to simulate dynamic programming behaviours on knapsack problem.
We show that they are able to compute an optimal solution within expected pseudo-polynomial time.
arXiv Detail & Related papers (2022-07-28T12:15:33Z) - Provably Faster Algorithms for Bilevel Optimization [54.83583213812667]
Bilevel optimization has been widely applied in many important machine learning applications.
We propose two new algorithms for bilevel optimization.
We show that both algorithms achieve the complexity of $mathcalO(epsilon-1.5)$, which outperforms all existing algorithms by the order of magnitude.
arXiv Detail & Related papers (2021-06-08T21:05:30Z) - A Two-stage Framework and Reinforcement Learning-based Optimization
Algorithms for Complex Scheduling Problems [54.61091936472494]
We develop a two-stage framework, in which reinforcement learning (RL) and traditional operations research (OR) algorithms are combined together.
The scheduling problem is solved in two stages, including a finite Markov decision process (MDP) and a mixed-integer programming process, respectively.
Results show that the proposed algorithms could stably and efficiently obtain satisfactory scheduling schemes for agile Earth observation satellite scheduling problems.
arXiv Detail & Related papers (2021-03-10T03:16:12Z) - A bi-level encoding scheme for the clustered shortest-path tree problem
in multifactorial optimization [1.471992435706872]
The Clustered Shortest-Path Tree Problem (CluSPT) plays an important role in various types of optimization problems in real-life.
Recently, some Multifactorial Evolutionary Algorithm (MFEA) have been introduced to deal with the CluSPT.
This paper describes a MFEA-based approach to solve the CluSPT.
arXiv Detail & Related papers (2021-02-12T13:36:07Z) - Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
This paper investigates the classical integer least-squares problem which estimates signals integer from linear models.
The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning.
We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
arXiv Detail & Related papers (2021-01-07T08:00:02Z) - Quantum approximate algorithm for NP optimization problems with
constraints [12.294570891467604]
In this paper, we formalize different constraint types to equalities, linear inequalities, and arbitrary form.
Based on this, we propose constraint-encoding schemes well-fitting into the QAOA framework for solving NP optimization problems.
The implemented algorithms demonstrate the effectiveness and efficiency of the proposed scheme.
arXiv Detail & Related papers (2020-02-01T04:45:41Z) - Extreme Algorithm Selection With Dyadic Feature Representation [78.13985819417974]
We propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms.
We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation.
arXiv Detail & Related papers (2020-01-29T09:40:58Z)
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.