scholarly journals The metapopulation fitness criterion: Proof and perspectives

2009 ◽  
Vol 75 (2-3) ◽  
pp. 183-200 ◽  
Author(s):  
François Massol ◽  
Vincent Calcagno ◽  
Julien Massol
Keyword(s):  
Fitness Criterion

Human self-selection as a mechanism of human societal evolution: A critique of the cultural selection argument

2021 ◽  
pp. 136843102110497
Author(s):  
Shanyang Zhao
Keyword(s):  
Natural Selection ◽  
Reproductive Success ◽  
Social Research ◽  
Cultural Adaptations ◽  
Neolithic Revolution ◽  
Cultural Selection ◽  
Self Selection ◽  
New Type ◽  
Fitness Criterion ◽  
Societal Evolution

Natural selection is the main mechanism that drives the evolution of species, including human societies. Under natural selection, human species responds through genetic and cultural adaptations to internal and external selection pressures for survival and reproductive success. However, this theory is ineffective in explaining human societal evolution in the Holocene and a cultural selection argument has been made to remedy the theory. The present article provides a critique of the cultural selection argument and proposes an alternative conception that treats human self-selection as an emergent mechanism of human societal evolution characterized by a new type of selection pressure and a separate fitness criterion. Specifically, the evolution of human societies is divided into two major periods, each driven by a different mode of selection: natural selection acting on genes and cultures for survival and reproductive success prior to the Neolithic Revolution, and human self-selection acting on cultures – and potentially genes as well – for thrival and prosperous living after the Neolithic Revolution. The conditions for the transition from the first mode of selection to the second and the implications of this transition for social research are also discussed.

Genetic Programming

2011 ◽  
pp. 926-931 ◽  
Author(s):  
William H. Hsu
Keyword(s):  
Genetic Programming ◽  
Programming Languages ◽  
Genetic Material ◽  
Biological Evolution ◽  
Validation Data ◽  
Functional Programming Languages ◽  
Real World Problem ◽  
Problem Solving Environment ◽  
Fitness Criterion ◽  
The Given

Genetic programming (GP) is a sub-area of evolutionary computation first explored by John Koza (1992) and independently developed by Nichael Lynn Cramer (1985). It is a method for producing computer programs through adaptation according to a user-defined fitness criterion, or objective function. Like genetic algorithms, GP uses a representation related to some computational model, but in GP, fitness is tied to task performance by specific program semantics. Instead of strings or permutations, genetic programs are most commonly represented as variable-sized expression trees in imperative or functional programming languages, as grammars (O’Neill & Ryan, 2001), or as circuits (Koza et al., 1999). GP uses patterns from biological evolution to evolve programs: • Crossover: Exchange of genetic material such as program subtrees or grammatical rules • Selection: The application of the fitness criterion to choose which individuals from a population will go on to reproduce • Replication: The propagation of individuals from one generation to the next • Mutation: The structural modification of individuals To work effectively, GP requires an appropriate set of program operators, variables, and constants. Fitness in GP is typically evaluated over fitness cases. In data mining, this usually means training and validation data, but cases can also be generated dynamically using a simulator or directly sampled from a real-world problem solving environment. GP uses evaluation over these cases to measure performance over the required task, according to the given fitness criterion.

Monitoring Players’ Readiness Using Predicted Heart-Rate Responses to Soccer Drills

2018 ◽  
Vol 13 (10) ◽  
pp. 1273-1280 ◽  
Author(s):  
Mathieu Lacome ◽  
Ben Simpson ◽  
Nick Broad ◽  
Martin Buchheit
Keyword(s):  
Heart Rate ◽  
Global Positioning System ◽  
Daily Basis ◽  
Internal Load ◽  
Individual Level ◽  
Multiple Stepwise Regression Analysis ◽  
Global Positioning ◽  
Fitness Criterion ◽  
The Difference ◽  
The Individual

Purpose: To examine the ability of multivariate models to predict the heart-rate (HR) responses to some specific training drills from various global positioning system (GPS) variables and to examine the usefulness of the difference in predicted vs actual HR responses as an index of fitness or readiness to perform. Method: All data were collected during 1 season (2016–17) with players’ soccer activity recorded using 5-Hz GPS and internal load monitored using HR. GPS and HR data were analyzed during typical small-sided games and a 4-min standardized submaximal run (12 km·h−1). A multiple stepwise regression analysis was used to identify which combinations of GPS variables showed the largest correlations with HR responses at the individual level (HRACT, 149 [46] GPS/HR pairs per player) and was further used to predict HR during individual drills (HRPRED). Then, HR predicted was compared with actual HR to compute an index of fitness or readiness to perform (HRΔ, %). The validity of HRΔ was examined while comparing changes in HRΔ with the changes in HR responses to a submaximal run (HRRUN, fitness criterion) and as a function of the different phases of the season (with fitness being expected to increase after the preseason). Results: HRPRED was very largely correlated with HRACT (r = .78 [.04]). Within-player changes in HRΔ were largely correlated with within-player changes in HRRUN (r = .66, .50–.82). HRΔ very likely decreased from July (3.1% [2.0%]) to August (0.8% [2.2%]) and most likely decreased further in September (−1.5% [2.1%]). Conclusions: HRΔ is a valid variable to monitor elite soccer players’ fitness and allows fitness monitoring on a daily basis during normal practice, decreasing the need for formal testing.

Why did life emerge?

2008 ◽  
Vol 7 (3-4) ◽  
pp. 293-300 ◽  
Author(s):  
Arto Annila ◽  
Erkki Annila
Keyword(s):  
Driving Force ◽  
Second Law Of Thermodynamics ◽  
Equation Of Motion ◽  
Energy Transduction ◽  
External Energy ◽  
Demarcation Line ◽  
Theory Of Evolution ◽  
Transduction Mechanisms ◽  
Fitness Criterion ◽  
Evolution By Natural Selection

AbstractMany mechanisms, functions and structures of life have been unraveled. However, the fundamental driving force that propelled chemical evolution and led to life has remained obscure. The second law of thermodynamics, written as an equation of motion, reveals that elemental abiotic matter evolves from the equilibrium via chemical reactions that couple to external energy towards complex biotic non-equilibrium systems. Each time a new mechanism of energy transduction emerges, e.g., by random variation in syntheses, evolution prompts by punctuation and settles to a stasis when the accessed free energy has been consumed. The evolutionary course towards an increasingly larger energy transduction system accumulates a diversity of energy transduction mechanisms, i.e. species. The rate of entropy increase is identified as the fitness criterion among the diverse mechanisms, which places the theory of evolution by natural selection on the fundamental thermodynamic principle with no demarcation line between inanimate and animate.

Genetic Programming

2011 ◽  
pp. 293-307
Author(s):  
William H. Hsu
Keyword(s):  
Genetic Programming ◽  
Objective Function ◽  
Evolutionary Computation ◽  
Computer Programs ◽  
Fitness Criterion

Genetic programming (GP) is a subarea of evolutionary computation first explored by John Koza (1992) and independently developed by Nichael Lynn Cramer (1985). It is a method for producing computer programs through adaptation according to a user-defined fitness criterion, or objective function.

GENETIC PROGRAMMING APPROACH FOR MULTI-CATEGORY PATTERN CLASSIFICATION APPLIED TO NETWORK INTRUSIONS DETECTION

2006 ◽  
Vol 06 (01) ◽  
pp. 77-99 ◽  
Author(s):  
K. M. FARAOUN ◽  
A. BOUKELIF
Keyword(s):  
Genetic Programming ◽  
Classification Problem ◽  
Programming Approach ◽  
Genetic Approach ◽  
Nonlinear Transformations ◽  
New Approach ◽  
Network Intrusions ◽  
Reduced Dimension ◽  
New Space ◽  
Fitness Criterion

The present paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically co-evolve a population of nonlinear transformations on the input data to be classified, and map them to a new space with reduced dimension in order to get a maximum inter-classes discrimination. It is much easier to classify the new samples from the transformed data. Contrary to the existing GP-classification techniques, the proposed one uses a dynamic repartition of the transformed data in separated intervals, the efficiency of a given intervals repartition is handled by the fitness criterion, with a maximum classes discrimination. Experiments were performed using the Fisher's Iris dataset. After that, the KDD'99 Cup dataset was used to study the intrusion detection and classification problem. The results demonstrate that the proposed genetic approach outperforms the existing GP-classification methods, and provides improved results compared to other existing techniques.

PMCNS

2014 ◽  
Vol 4 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Jorge Gomes ◽  
Paulo Urbano ◽  
Anders Lyhne Christensen
Keyword(s):  
Space Exploration ◽  
Evolutionary Approach ◽  
Local Optima ◽  
Novelty Search ◽  
Fitness Criterion ◽  
Significant Effort

Novelty search is an evolutionary approach in which the population is driven towards behavioural innovation instead of towards a fixed objective. The use of behavioural novelty to score candidate solutions precludes convergence to local optima. However, in novelty search, significant effort may be spent on exploration of novel, but unfit behaviours. We propose progressive minimal criteria novelty search (PMCNS) to overcome this issue. In PMCNS, novelty search can freely explore the behaviour space as long as the solutions meet a progressively stricter fitness criterion. We evaluate the performance of our approach by evolving neurocontrollers for swarms of robots in two distinct tasks. Our results show that PMCNS outperforms fitness-based evolution and pure novelty search, and that PMCNS is superior to linear scalarisation of novelty and fitness scores. An analysis of behaviour space exploration shows that the benefits of novelty search are conserved in PMCNS despite the evolutionary pressure towards progressively fitter behaviours.

Genetic Programming

2011 ◽  
pp. 529-533
Author(s):  
William H. Hsu
Keyword(s):  
Genetic Programming ◽  
Objective Function ◽  
Evolutionary Computation ◽  
Computer Programs ◽  
Fitness Criterion

Genetic programming (GP) is a subarea of evolutionary computation first explored by John Koza (1992) and independently developed by Nichael Lynn Cramer (1985). It is a method for producing computer programs through adaptation according to a user-defined fitness criterion, or objective function.

Vox Populi: An Interactive Evolutionary System for Algorithmic Music Composition

2000 ◽  
Vol 10 ◽  
pp. 49-54 ◽  
Author(s):  
Artemis Moroni ◽  
Jônatas Manzolli ◽  
Fernando Von Zuben ◽  
Ricardo Gudwin
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Music Composition ◽  
Sound Synthesis ◽  
Physical Factors ◽  
Algorithmic Composition ◽  
Evolutionary System ◽  
Algorithmic Music ◽  
Fitness Criterion ◽  
Algorithmic Music Composition

While recent techniques of digital sound synthesis have put numerous new sounds on the musician's desktop, several artificial-intelligence (AI) techniques have also been applied to algorithmic composition. This article introduces Vox Populi, a system based on evolutionary computation techniques for composing music in real time. In Vox Populi, a population of chords codified according to MIDI protocol evolves through the application of genetic algorithms to maximize a fitness criterion based on physical factors relevant to music. Graphical controls allow the user to manipulate fitness and sound attributes.

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