scholarly journals Complexity Transitions in Evolutionary Algorithms: Evaluating the impact of the initial population

2005 ◽  
Author(s):  
A. Defaweux ◽  
T. Lenaerts ◽  
J. van Hemert ◽  
J. Parent
Keyword(s):  
Evolutionary Algorithms ◽  
Initial Population ◽  
The Impact

scholarly journals Investigating the Impact of Root-Lesion Nematodes (Pratylenchus thornei) and Crown Rot (Fusarium pseudograminearum) on Diverse Cereal Cultivars in a Conservation Farming System

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 867
Author(s):  
John P. Thompson ◽  
Timothy G. Clewett
Keyword(s):  
Grain Yield ◽  
Soil Profile ◽  
Crown Rot ◽  
Tolerance Index ◽  
Initial Population ◽  
Fusarium Pseudograminearum ◽  
Conservation Farming ◽  
Pratylenchus Thornei ◽  
Fourth Experiment ◽  
The Impact

In two experiments on a farm practicing conservation agriculture, the grain yield of a range of wheat cultivars was significantly (p < 0.001) negatively related to the post-harvest population densities of Pratylenchus thornei in the soil profile to 45 cm depth. In a third and fourth experiment with different rotations, methyl bromide fumigation significantly (p < 0.05) decreased (a) a low initial population density of P. thornei in the soil profile to 90 cm depth and (b) a high initial population of P. thornei to 45 cm depth, and a medium level of the crown rot fungus, Fusarium pseudograminearum, at 0–15 cm depth to a low level. For a range of wheat and durum cultivars, grain yield and response to fumigation were highly significantly (p < 0.001) related to (a) the P. thornei tolerance index of the cultivars in the third experiment, and (b) to both the P. thornei tolerance index and the crown rot resistance index in the fourth experiment. In the latter, grain yield was significantly (p < 0.001) positively related to biomass at anthesis and negatively related to percentage whiteheads at grain fill growth stage. One barley cultivar was more tolerant to both diseases than the wheat and durum cultivars. Crop rotation, utilizing crop cultivars resistant and tolerant to both P. thornei and F. pseudograminearum, is key to success for conservation farming in this region.

scholarly journals Influence of plants used in agricultural diversification on the nematode Heterorhabditis amazonensis

2021 ◽  
Vol 42 (6supl2) ◽  
pp. 3553-3566
Author(s):  
Natalia Ramos Mertz ◽  
◽  
Fernanda Soares Sales ◽  
Elsa Judith Guevara Agudelo ◽  
Alcides Moino Junior ◽  
...  
Keyword(s):  
Entomopathogenic Nematodes ◽  
Control Treatment ◽  
Agricultural System ◽  
Tagetes Erecta ◽  
Initial Population ◽  
Agricultural Diversification ◽  
Main Crop ◽  
Short Time ◽  
The Impact ◽  
Crotalaria Spectabilis

In an agricultural system, to increase natural biological control, plants that attract natural enemies can be grown alongside the main crop. However, the effects of these plants on entomopathogenic nematodes (EPNs), important agents for controlling soil pests, and the action of their conservation are unknown. To assess the impact of these plants on EPNs, two experiments were carried out in a greenhouse. The first measured the effect of Crotalaria spectabilis, Crotalaria breviflora, and Tagetes erecta on the persistence and infectivity of Heterorhabditis amazonensis isolate RSC 5 for 27 days, compared to a control treatment without plants. The second trial evaluated the effect of C. breviflora and T. erecta on the displacement of the nematode. Additionally, the influence of predator Calosoma granulatum in this system was evaluated. The plants did not influence nematode behaviour in terms of persistence, infectivity, or displacement. However, C. spectabilis allowed the most significant persistence of nematodes in the substrate for a short time, and T. erecta caused the fastest suppression of the initial population of infectives juvenile. In the second experiment, neither the predator nor the plants affected the nematode’s ability to move in the soil within 5 days. These results show that prior knowledge in agricultural diversification can help to control pests by inundative application of EPNs.

scholarly journals A theoretical framework for multi-species range expansion in spatially heterogeneous landscapes

2021 ◽  
Author(s):  
Lukas Eigentler ◽  
Nicola R Stanley-Wall ◽  
Fordyce A Davidson
Keyword(s):  
Population Density ◽  
Range Expansion ◽  
Theoretical Framework ◽  
Dominant Mode ◽  
Bacterial Biofilm ◽  
Time Interval ◽  
Initial Population ◽  
Initial Population Density ◽  
Competitive Outcome ◽  
The Impact

Range expansion is the spatial spread of a population into previously unoccupied regions. Understanding range expansion is important for the study and successful manipulation and management of ecosystems, with applications ranging from controlling bacterial biofilm formation in industrial and medical environments to large scale conservation programmes for species undergoing climate-change induced habitat disruption. During range expansion, species typically encounter competitors. Moreover, the environment into which expansion takes place is almost always heterogeneous when considered at the scale of the individual. Despite the ubiquitous nature of these features, the impact of competition and spatial landscape heterogeneities on range expansion remains understudied. In this paper we present a theoretical framework comprising two competing generic species undergoing range expansion and use it to investigate the impact of spatial landscape heterogeneities on range expansion with a particular focus on its effect on competition dynamics. We reveal that the area covered by range expansion during a fixed time interval is highly variable due to the fixed landscape heterogeneities. Moreover, we report significant variability in competitive outcome (relative abundance of a focal species) but determine that this is induced by low initial population densities, independent of landscape heterogeneities. We further show that both area covered by range expansion and competitive outcome can be accurately predicted by a Voronoi tessellation with respect to an appropriate metric, which only requires information on the spatial landscape and the response of each species to that landscape. Finally, we reveal that if species interact antagonistically during range expansion, the dominant mode of competition depends on the initial population density. Antagonistic actions determine competitive outcome if the initial population density is high, but competition for space is the dominant mode of competition if the initial population density is low.

scholarly journals A Revisit of Infinite Population Models for Evolutionary Algorithms on Continuous Optimization Problems

2020 ◽  
Vol 28 (1) ◽  
pp. 55-85
Author(s):  
Bo Song ◽  
Victor O.K. Li
Keyword(s):  
Population Dynamics ◽  
Evolutionary Algorithms ◽  
Population Size ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Analytical Framework ◽  
Population Models ◽  
Initial Population ◽  
Infinite Population ◽  
Continuous Optimization Problems

Infinite population models are important tools for studying population dynamics of evolutionary algorithms. They describe how the distributions of populations change between consecutive generations. In general, infinite population models are derived from Markov chains by exploiting symmetries between individuals in the population and analyzing the limit as the population size goes to infinity. In this article, we study the theoretical foundations of infinite population models of evolutionary algorithms on continuous optimization problems. First, we show that the convergence proofs in a widely cited study were in fact problematic and incomplete. We further show that the modeling assumption of exchangeability of individuals cannot yield the transition equation. Then, in order to analyze infinite population models, we build an analytical framework based on convergence in distribution of random elements which take values in the metric space of infinite sequences. The framework is concise and mathematically rigorous. It also provides an infrastructure for studying the convergence of the stacking of operators and of iterating the algorithm which previous studies failed to address. Finally, we use the framework to prove the convergence of infinite population models for the mutation operator and the [Formula: see text]-ary recombination operator. We show that these operators can provide accurate predictions for real population dynamics as the population size goes to infinity, provided that the initial population is identically and independently distributed.

Comparative Study of Evolutionary Algorithms for Parameter Identification of an Impact Oscillator

2014 ◽  
Author(s):  
Amit Banerjee ◽  
Issam Abu Mahfouz
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Parameter Identification ◽  
Optimization Techniques ◽  
System Response ◽  
Impact Oscillator ◽  
Swarm Optimization ◽  
System Parameters ◽  
Highly Nonlinear ◽  
The Impact

The use of non-classical evolutionary optimization techniques such as genetic algorithms, differential evolution, swarm optimization and genetic programming to solve the inverse problem of parameter identification of dynamical systems leading to chaotic states has been gaining popularity in recent years. In this paper, three popular evolutionary algorithms — differential evolution, particle swarm optimization and the firefly algorithm are used for parameter identification of a clearance-coupled-impact oscillator system. The behavior of impacting systems is highly nonlinear exhibiting a myriad of harmonic, low order and high order sub-harmonic resonances, as well as chaotic vibrations. The time-history simulations of the single-degree-of-freedom impact oscillator were obtained by the Neumark-β numerical integration algorithm. The results are illustrated by bifurcation graphs, state space portraits and Poincare’ maps which gives valuable insights on the dynamics of the impact system. The parameter identification problem relates to finding one set of system parameters given a chaotic or periodic system response as a set of Poincaré points and a different but known set of system parameters. The three evolutionary algorithms are compared over a set of parameter identification problems. The algorithms are compared based on solution quality to evaluate the efficacy of using one algorithm over another.

Effective Metaheuristic Algorithms for Bag-of-Tasks Scheduling Problems Under Budget Constraints on Hybrid Clouds

2020 ◽  
pp. 2150091
Author(s):  
Linhua Ma ◽  
Chunshan Xu ◽  
Haoyang Ma ◽  
Yujie Li ◽  
Jiali Wang ◽  
...  
Keyword(s):  
Initial Solution ◽  
Metaheuristic Algorithms ◽  
Initial Population ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Task Sequence ◽  
Hybrid Clouds ◽  
Data Intensive ◽  
Communication Time ◽  
The Impact

Cloud computing is an ideal platform for executing bag-of-task (BoT) applications due to its capability of delivering high-quality and pay-per-use computing services. This paper presents a family of genetic algorithm (GA)-based metaheuristics for scheduling the tasks of data-intensive BoT applications on hybrid clouds. The scheduling objective is to minimize the flowtime of BoT applications under a specified budget constraint. We take into account the impact of communication time and communication cost to formulate the optimization model for the data-intensive BoT scheduling problem. By using a task sequence to represent the scheduling solution, the proposed algorithms start with using a low-complexity strategy to generate an initial solution. The generated initial solution is identified as the best chromosome in the initial population of GA framework. We improve the standard crossover operator in GA’s evolutionary procedure by incorporating a probabilistic model. In addition, we design an efficient task dispatching method to evaluate the scheduling quality of each chromosome. Built upon the improved crossover scheme and task dispatching method, the proposed metaheuristic algorithms employ three crossover operators to solve the BoT scheduling problem considered in this work. Extensive experiments are performed to verify the performance of the proposed algorithms in scheduling data-intensive BoT applications.

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