Fireworks Algorithm versus Plant Propagation Algorithm

Vrielink, Wouter; Berg, Daan van den

doi:10.5220/0008169401010112

“…The found extremely hard instances were structured, and abiding by the teachings of A. N. Kolmogorov, structured objects in large randomized ensembles are rare [40]. So finding these instances paradoxically means knowing where to look, and one way to do that is to use a parameter-unsensitive evolutionary algorithm such as Plant Propagation [41] [42] [43] [44] [45]. For very large graphs, one could better resort to a single-individual search heuristic, such as HillClimbing or simulated annealing [46] [47] [48].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Backtracking (the) Algorithms on the Hamiltonian Cycle Problem

Sleegers¹,

Berg²

2021

Preprint

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Even though the Hamiltonian cycle problem is NPcomplete, many of its problem instances aren't. In fact, almost all the hard instances reside in one area: near the Komlós-Szemerédi bound, ofedges, where randomly generated graphs have an approximate 50% chance of being Hamiltonian. If the number of edges is either much higher or much lower, the problem is not hard -most backtracking algorithms decide such instances in (near) polynomial time. Recently however, targeted search efforts have identified very hard Hamiltonian cycle problem instances very far away from the Komlós-Szemerédi bound. In that study, the used backtracking algorithm was Vandegriend-Culberson's, which was supposedly the most efficient of all Hamiltonian backtracking algorithms.In this paper, we make a unified large scale quantitative comparison for the best known backtracking algorithms described between 1877 and 2016. We confirm the suspicion that the Komlós-Szemerédi bound is a hard area for all backtracking algorithms, but also that Vandegriend-Culberson is indeed the most efficient algorithm, when expressed in consumed computing time. When measured in recursive effectiveness however, the algorithm by Frank Rubin, almost half a century old, performs best. In a more general algorithmic assessment, we conjecture that edge pruning and non-Hamiltonicity checks might be largely responsible for these recursive savings. When expressed in system time however, denser problem instances require much more time per recursion. This is most likely due to the costliness of the extra search pruning procedures, which are relatively elaborate. We supply large amounts of experimental data, and a unified single-program implementation for all six algorithms. All data and algorithmic source code is made public for further use by our colleagues.

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“…All 14 functions resulting in these 59 instances can be explicitly found in Table 1 (and some in Figure 1), each with its global minimum and domain of definition. It should be noted that this suite is identical to many other PPA-studies [9,10,34,[40][41][42], facilitating connections beyond this study alone, and abiding by 'best practices' in benchmarking [2]. Each run counts 50,000 function evaluations and is repeated 15 times, for all 8 selection methods, on all 59 function instances, resulting in 7080 runs with a total of 354 million function evaluations for the entire experiment.…”

Section: Methodsmentioning

confidence: 99%

“…Despite sometimes aspiring to the Darwinian ideology of "survival of the fittest", one should realize that optimality of natural evolution itself, and thereby the philosophical The nine n-dimensional benchmark functions used in this experiment, in their 2D forms. The benchmark suite as a whole is used in several other studies and contains a variety of characteristics such as (multi)modality, differently shaped and shifted minima, plateaus, and various degrees of convexity [40]. Images of the other five functions can be found in the public repository [4] foundation of evolutionary algorithms, can easily be challenged [28,32].…”

Section: Introductionmentioning

confidence: 99%

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Survivor selection in a crossoverless evolutionary algorithm

Brouwer

¹

,

Dijkzeul

²

,

Koppenhol

³

et al. 2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

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“…Whereas Lital's is an exact algorithm guaranteeing solution optimality, the plant propagation algorithm (PPA) is a parameterized metaheuristic, much akin to a genetic algorithm, but without a crossover operator [49][68] [69]. Being population-based, it revolves around the idea that fitter individuals produce more offspring with fewer mutations, while unfitter individuals produce fewer offspring with more mutations, all in an effort to balance the forces of exploration and exploitation when traversing the combinatorial state space.…”

Section: Plant Propagation Algorithmmentioning

confidence: 99%

Exactly characterizable parameter settings in a crossoverless evolutionary algorithm

Koppenhol

¹

,

Brouwer

²

,

Dijkzeul

³

et al. 2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

Self Cite

View full text Add to dashboard Cite

General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

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Fireworks Algorithm versus Plant Propagation Algorithm

Cited by 7 publications

References 13 publications

Backtracking (the) Algorithms on the Hamiltonian Cycle Problem

Backtracking (the) Algorithms on the Hamiltonian Cycle Problem

Survivor selection in a crossoverless evolutionary algorithm

Exactly characterizable parameter settings in a crossoverless evolutionary algorithm

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