A Meta-Heuristic Search Algorithm based on Infrasonic Mating Displays in Peafowls

• URL: http://arxiv.org/abs/2106.14487v1
• Date: Mon, 28 Jun 2021 09:04:51 GMT
• Title: A Meta-Heuristic Search Algorithm based on Infrasonic Mating Displays in Peafowls
• Authors: Patrick Kenekayoro
• Abstract summary: Simplistic methods such as exhaustive search become computationally expensive and unreliable as the solution space for search algorithms increase. This research proposes an Infrasonic Search Algorithm, inspired from the Gravitational Search Algorithm and the mating behaviour in peafowls.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Meta-heuristic techniques are important as they are used to find solutions to computationally intractable problems. Simplistic methods such as exhaustive search become computationally expensive and unreliable as the solution space for search algorithms increase. As no method is guaranteed to perform better than all others in all classes of optimization search problems, there is a need to constantly find new and/or adapt old search algorithms. This research proposes an Infrasonic Search Algorithm, inspired from the Gravitational Search Algorithm and the mating behaviour in peafowls. The Infrasonic Search Algorithm identified competitive solutions to 23 benchmark unimodal and multimodal test functions compared to the Genetic Algorithm, Particle Swarm Optimization Algorithm and the Gravitational Search Algorithm.

Related papers

• A Three-Stage Algorithm for the Closest String Problem on Artificial and Real Gene Sequences [39.58317527488534]
  Closest String Problem is an NP-hard problem that aims to find a string that has the minimum distance from all sequences that belong to the given set of strings. In this paper, we introduce a three-stage algorithm that comprises the following process: first, we apply a novel alphabet pruning method to reduce the search space for effectively finding promising search regions. Second, a variant of beam search to find a solution is employed. This method utilizes a newly developed guiding function based on an expected distance score of partial solutions.
  arXiv  Detail & Related papers  (2024-07-17T21:26:27Z)
• MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge [110.7678032481059]
  We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings.
  arXiv  Detail & Related papers  (2022-09-22T16:44:59Z)
• Machine Learning for Online Algorithm Selection under Censored Feedback [71.6879432974126]
  In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. We adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon.
  arXiv  Detail & Related papers  (2021-09-13T18:10:52Z)
• Algorithm Selection on a Meta Level [58.720142291102135]
  We introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework.
  arXiv  Detail & Related papers  (2021-07-20T11:23:21Z)
• 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)
• Critical Analysis: Bat Algorithm based Investigation and Application on Several Domains [1.1802674324027231]
  The idea of the algorithm was taken from the echolocation ability of bats. Bat Algorithm is given in-depth in terms of backgrounds, characteristics, limitations.
  arXiv  Detail & Related papers  (2021-01-18T19:25:12Z)
• 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 Search with Prior Knowledge [15.384459603233978]
  We propose a new generalization of Grover's search algorithm which performs better than the standard Grover algorithm in average under this setting. We prove that our new algorithm achieves the optimal expected success probability of finding the solution if the number of queries is fixed.
  arXiv  Detail & Related papers  (2020-09-18T09:50:33Z)
• Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples [10.993342896547691]
  We study the behavior of several black-box search algorithms used for generating adversarial examples for natural language processing (NLP) tasks. We perform a fine-grained analysis of three elements relevant to search: search algorithm, search space, and search budget.
  arXiv  Detail & Related papers  (2020-09-09T17:04:42Z)
• Performance Analysis of Meta-heuristic Algorithms for a Quadratic Assignment Problem [6.555180412600522]
  A quadratic assignment problem (QAP) is an optimization problem that belongs to the class of NP-hard ones. Heuristics and meta-heuristics algorithm are prevalent solution methods for this problem. This paper is one of comparative studies to apply different metaheuristic algorithms for solving the QAP.
  arXiv  Detail & Related papers  (2020-07-29T15:02:07Z)
• 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.