Recruitment prediction with genetic algorithms with application to the Pacific Herring fishery

Dreyfus-León, Michel; Chen, D.G.

doi:10.1016/j.ecolmodel.2005.09.016

Cited by 18 publications

(7 citation statements)

References 3 publications

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“…An approach to predict the levels of the recruitment based on generic algorithms in Fisheries with an implementation of artificial intelligence related to spawning biomass resulting to be 61.9 per cent predicting correctly at 5 per cent mutation rate. It revealed that the spawning biomass wasn't significant for the establishment of recruitment level rather was based on the environment [6]. The emphasis of the study is on the recruitment theory focused on the communicative and cognitive economy built on the neural mechanisms with different strategies to address the challenges of communication during the process of recruitment [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence: A Technological Prototype in Recruitment

Vedapradha¹,

Ravi²,

Shivakami³

2019

JSSM

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Purpose: The study is conducted to evaluate the adaptability of artificial intelligence in recruitment and to assess the effect of this technology on the performance of the employees. Design/Methodology/Approach: Standard Multiple Linear regression model is used to predict the performance of the employees and one-way ANOVA is used to compare the artificial intelligence based recruitment with performance indicating variables namely reliability, productivity, Automation, Gamification & Training using SPSS. Snowball sampling method has been adopted for a sample size of 440 respondents working in leading recruitment consultancies in urban Bangalore. Findings: There is a greater association between the recruitment and performance variables when artificial intelligence is adopted as it is significant at 0.001 per cent level and productivity being the maximum. However, the impact of implementing gamification for recruitment doesn't have a significant impact on the output due to partial significant effect on the adoption as (p = 0.046 < 0.05). Value of "R" is 0.604 and the coefficient of determination is 0.365. Productivity, Training, Automation & Reliability are the significant predictors of the performance in employees. Originality/Value: Artificial intelligence has emerged as a boon to the recruiters by automating the repetitive tasks, administrative tasks. Intelligent screening helps in automating resume screening, recruiter Chatbots for real-time candidate engagement, and digitization of interviews. This promotes pro-active strategic decision making better by the recruiters.

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Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence: A Technological Prototype in Recruitment

Vedapradha¹,

Ravi²,

Shivakami³

2019

JSSM

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“…Tester discovered an inverse relationship between year‐class strength and July air temperature during the first year of life. There has been a succession of reports (Schweigert, ; Chen and Ware, ; Chen et al ., ; Chen and Irvine, ; Dreyfus‐León and Chen, ) that progressed from investigating recruitment variability using correlation analysis to predicting recruitment using genetic algorithms. These studies used stock assessment model estimates of recruitment and spawning biomass assumed that physical oceanographic measures were proxies for prey variability, and included Ware and McFarlane's () description of the negative effect of the biomass of Pacific hake on recruitment.…”

Section: Recruitmentmentioning

confidence: 99%

An investigation of the biological basis of recruitment, growth and adult survival rate variability of Pacific herring (Clupea pallasi) from British Columbia: a synthesis

Tanasichuk

2017

Fisheries Oceanography

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I explored the biological basis of variation in recruitment (age 3 abundance), growth and age‐specific adult survival rate for the major populations [West Coast Vancouver Island (WCVI), Strait of Georgia, Central Coast, North Coast and Haida Gwaii] of Pacific herring (Clupea pallasi) that inhabit British Columbian waters. The analyses were based on a synthesis of time series of empirical observations of herring population characteristics (egg deposition, age‐specific abundance and size) and prey, competitor and predator biomass/abundance. Recruitment was not correlated among populations. Recruitment variability was explained for WCVI herring only, as a consequence of prey (the euphausiid Thysanoessa spinifera) biomass during August in each of the first 3 years of life, and the biomass of piscivorous Pacific hake (Merluccius productus) during the first year of life. Recruit mass and adult mass‐at‐age were correlated among populations and over ages within populations. Recruit mass was affected by T. spinifera biomass in August of the first and third years of life. Adult mass‐at‐age variability was determined mainly by size at the beginning of the growth season, but also by T. spinifera biomass in August. Age‐specific adult survival rates were not correlated among the five populations. Survival rates decreased with age; there were additional population‐specific effects of somatic mass and T. spinifera biomass in August. The analyses were repeated using physical oceanographic explanatory variables. Only recruit mass variation was explained significantly by physical oceanographic variables, and the biological‐based explanation of recruit mass variability accounted for more of the variation.

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“…A large number of studies have been undertaken using different techniques, to utilize such environmental information to predict recruitment (e.g., Chen and Ware, 1999;Bailey et al, 2005;Dreyfus-León and Chen, 2007;Dreyfus-León and Schweigert, 2008;MacKenzie et al, 2008;Ruiz et al, 2009). Nevertheless, the recruitment problem remains difficult because the mechanisms behind such complex relationships are often poorly understood; this in turn leading to the failure of some proposed relationships, methods, and performance estimations, when new data become available (Myers et al, 1995).…”

Section: Introductionmentioning

confidence: 99%

“…It is difficult to clarify and model the mechanisms controlling recruitment by using conventional methods because fish populations have complex and non-linear response, and reactions to biotic interreactions and environmental changes (Olden and Jackson, 2001). Machine-learning techniques such as Artificial Neural Networks (ANNs) have been proposed as "an appropriate approach with some desirable properties to address such problems" (Dreyfus-León and Chen, 2007;Uusitalo, 2007;Fernandes et al, 2013). ANNs gained momentum in mid-1980s (Rumelhart et al, 1986) and subsequently they have been used in a variety of ecological applications.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Role of Environmental Factors on Baltic Cod Recruitment, a Complex Adaptive System Emergent Property

2016

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For decades, fish recruitment has been a subject of intensive research with stock-recruitment models commonly used for recruitment prediction often only explaining a small fraction of the inter-annual recruitment variation. The use of environmental information to improve our ability to predict recruitment, could contribute considerably to fisheries management. However, the problem remains difficult because the mechanisms behind such complex relationships are often poorly understood; this in turn, makes it difficult to determine the forecast estimation robustness, leading to the failure of some relationships when new data become available. The utility of machine learning algorithms such as artificial neural networks (ANNs) for solving complex problems has been demonstrated in aquatic studies and has led many researchers to advocate ANNs as an attractive, non-linear alternative to traditional statistical methods. The goal of this study is to design a Baltic cod recruitment model (FishANN) that can account for complex ecosystem interactions. To this end, we (1) build a quantitative model representation of the conceptual understanding of the complex ecosystem interactions driving Baltic cod recruitment dynamics, and (2) apply the model to strengthen the current capability to project future changes in Baltic cod recruitment. FishANN is demonstrated to bring multiple stressors together into one model framework and estimate the relative importance of these stressors while interpreting the complex non-linear interactions between them. Additional requirements to further improve the current study in the future are also proposed.

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Recruitment prediction with genetic algorithms with application to the Pacific Herring fishery

Cited by 18 publications

References 3 publications

Artificial Intelligence: A Technological Prototype in Recruitment

Artificial Intelligence: A Technological Prototype in Recruitment

An investigation of the biological basis of recruitment, growth and adult survival rate variability of Pacific herring (Clupea pallasi) from British Columbia: a synthesis

Assessing the Role of Environmental Factors on Baltic Cod Recruitment, a Complex Adaptive System Emergent Property

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