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Computing diverse sets of high-quality solutions has gained increasing attention among the evolutionary computation community in recent years. It allows practitioners to choose from a set of high-quality alternatives. In this paper, we…

Neural and Evolutionary Computing · Computer Science 2021-04-29 Adel Nikfarjam , Jakob Bossek , Aneta Neumann , Frank Neumann

Evolutionary algorithms have been shown to obtain good solutions for complex optimization problems in static and dynamic environments. It is important to understand the behaviour of evolutionary algorithms for complex optimization problems…

Neural and Evolutionary Computing · Computer Science 2023-05-31 Jakob Bossek , Aneta Neumann , Frank Neumann

Evolutionary algorithms based on edge assembly crossover (EAX) constitute some of the best performing incomplete solvers for the well-known traveling salesperson problem (TSP). Often, it is desirable to compute not just a single solution…

Neural and Evolutionary Computing · Computer Science 2021-08-13 Adel Nikfarjam , Jakob Bossek , Aneta Neumann , Frank Neumann

Diversity plays a crucial role in evolutionary computation. While diversity has been mainly used to prevent the population of an evolutionary algorithm from premature convergence, the use of evolutionary algorithms to obtain a diverse set…

Neural and Evolutionary Computing · Computer Science 2018-02-16 Aneta Neumann , Wanru Gao , Carola Doerr , Frank Neumann , Markus Wagner

In this work, we consider the problem of finding a set of tours to a traveling salesperson problem (TSP) instance maximizing diversity, while satisfying a given cost constraint. This study aims to investigate the effectiveness of applying…

Neural and Evolutionary Computing · Computer Science 2022-04-20 Anh Viet Do , Mingyu Guo , Aneta Neumann , Frank Neumann

There has been a growing interest in the evolutionary computation community to compute a diverse set of high-quality solutions for a given optimisation problem. This can provide the practitioners with invaluable information about the…

Neural and Evolutionary Computing · Computer Science 2022-04-07 Adel Nikfarjam , Aneta Neumann , Frank Neumann

Generating diverse populations of high quality solutions has gained interest as a promising extension to the traditional optimization tasks. This work contributes to this line of research with an investigation on evolutionary diversity…

Neural and Evolutionary Computing · Computer Science 2022-11-01 Anh Viet Do , Mingyu Guo , Aneta Neumann , Frank Neumann

Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem. Many studies have considered diversity 1) as a mean to explore niches in…

Neural and Evolutionary Computing · Computer Science 2022-07-29 Adel Nikfarjam , Aneta Neumann , Jakob Bossek , Frank Neumann

Several important optimization problems in the area of vehicle routing can be seen as a variant of the classical Traveling Salesperson Problem (TSP). In the area of evolutionary computation, the traveling thief problem (TTP) has gained…

Neural and Evolutionary Computing · Computer Science 2020-02-05 Jakob Bossek , Katrin Casel , Pascal Kerschke , Frank Neumann

In this work we introduce an evolutionary strategy to solve combinatorial optimization tasks, i.e. problems characterized by a discrete search space. In particular, we focus on the Traveling Salesman Problem (TSP), i.e. a famous problem…

Disordered Systems and Neural Networks · Physics 2016-08-05 Marco Alberto Javarone

Diversification in a set of solutions has become a hot research topic in the evolutionary computation community. It has been proven beneficial for optimisation problems in several ways, such as computing a diverse set of high-quality…

Neural and Evolutionary Computing · Computer Science 2022-07-29 Adel Nikfarjam , Amirhossein Moosavi , Aneta Neumann , Frank Neumann

We address the Diverse Traveling Salesman Problem (D-TSP), a bi-criteria optimization challenge that seeks a set of $k$ distinct TSP tours. The objective requires every selected tour to have a length at most $c|T^*|$ (where $|T^*|$ is the…

Computational Geometry · Computer Science 2026-01-12 Hao-Tsung Yang , Ssu-Yuan Lo , Kuan-Lun Chen , Ching-Kai Wang

In real-world optimisation, it is common to face several sub-problems interacting and forming the main problem. There is an inter-dependency between the sub-problems, making it impossible to solve such a problem by focusing on only one…

Neural and Evolutionary Computing · Computer Science 2023-01-05 Adel Nikfarjam , Aneta Neumann , Frank Neumann

This paper proposes a hybrid genetic algorithm for solving the Multiple Traveling Salesman Problem (mTSP) to minimize the length of the longest tour. The genetic algorithm utilizes a TSP sequence as the representation of each individual,…

Neural and Evolutionary Computing · Computer Science 2023-10-31 Sasan Mahmoudinazlou , Changhyun Kwon

The genetic algorithm includes some parameters that should be adjusted, so as to get reliable results. Choosing a representation of the problem addressed, an initial population, a method of selection, a crossover operator, mutation…

Neural and Evolutionary Computing · Computer Science 2012-03-15 Otman Abdoun , Jaafar Abouchabaka , Chakir Tajani

The Traveling Salesman Problem (TSP) is among the most famous NP-hard optimization problems. We design for this problem a randomized polynomial-time algorithm that computes a (1+eps)-approximation to the optimal tour, for any fixed eps>0,…

Computational Complexity · Computer Science 2016-09-09 Yair Bartal , Lee-Ad Gottlieb , Robert Krauthgamer

TSP (Traveling Salesman Problem), a classic NP-complete problem in combinatorial optimization, is of great significance in multiple fields. Exact algorithms for TSP are not practical due to their exponential time cost. Thus, approximate…

Data Structures and Algorithms · Computer Science 2019-11-12 Yang Li , Junbin Gao , Mingyuan Bai , Chengjun Li , Gang Liu

Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Traveling Salesperson problem. In this paper, we present a general framework that is able to…

Neural and Evolutionary Computing · Computer Science 2020-06-01 Wanru Gao , Samadhi Nallaperuma , Frank Neumann

The Traveling Thief Problem (TTP) is a multi-component optimization problem that captures the interplay between routing and packing decisions by combining the classical Traveling Salesperson Problem (TSP) and the Knapsack Problem (KP). The…

Data Structures and Algorithms · Computer Science 2026-04-22 Jan Eube , Kelin Luo , Aneta Neumann , Frank Neumann , Heiko Röglin

The Travelling Salesman and its variations are some of the most well known NP hard optimisation problems. This paper looks to use both centralised and decentralised implementations of Evolutionary Algorithms (EA) to solve a dynamic variant…

Neural and Evolutionary Computing · Computer Science 2019-06-14 Thomas E. Kent , Arthur G. Richards
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