1 / 26

ING models: how they work and how they are constructed

ING models: how they work and how they are constructed. I ndividual based N eural network G enetic algoritm by Espen Strand and Geir Huse. ING models - Presentation layout:. Representation of individuals Attribute and strategy vector, super-individual The genetic algorithm in ING models

flo
Download Presentation

ING models: how they work and how they are constructed

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. ING models:how they work and how they are constructed Individual based Neural network Genetic algoritm by Espen Strand and Geir Huse

  2. ING models - Presentation layout: • Representation of individuals • Attribute and strategy vector, super-individual • The genetic algorithm in ING models • Structure, initiation, selection vs. variability, reproduction • Model constraints (avoiding Darwinian monsters) • Fitness in ING models • The neural network • Network architecture, types of input, stimuli transformation • One example of an ING model

  3. Strategy vector (length n) 3.2 1.3 -0.3 2.7 -4.1 2.3 0.1 1.0 …. n 1.6 kg 590 days 34g fat female 303 eggs Attribute vector The individuals • All individuals are numerically described by a unique strategy vector (easy think of it as genes): • All individuals’ states are described in the attribute vector:

  4. 500 ind 590 days 34g fat female 303 eggs Attribute vector Super-individuals • There is, depending on model complexity, an upper practical limit to how many individuals that can be simulated • In models where the number or biomass of individuals are important and very high, a way around this problem is to treat each individual as a super-individual • A super-individual simply has a number added to its attribute vector telling how many (identical) individuals it represents

  5. The genetic algorithm (GA) • A GA is an algorithm that mimics evolution by natural selection. So - what is required to make evolution possible? • A population of individuals • Genetic variability among individuals • A genotype – phenotype relationship • Individual variation in phenotypic success (fitness) • Inheritability of genotypes from one generation to the next • Introduction of new genetic variance (at least in the long run) • How is this implemented in a GA?

  6. A population of individuals • Genetic variability among individuals • A genotype – phenotype relationship • Individual variation in fitness • Inheritability of genotypes from one generation to the next • Introduction of new genetic variance 1 2 3 … … N Implementing a GA - I Strategy vector (length n) Population (size N)

  7. A population of individuals • Genetic variability among individuals • A genotype – phenotype relationship • Individual variation in fitness • Inheritability of genotypes from one generation to the next • Introduction of new genetic variance Neural network Input 1 Input 2 Input 3 Input 4 Input 5 1 Behaviour 3.2 1.3 -0.3 2.7 -4.1 2.3 0.1 1.0 -2.1 0.5 Depth 2 Strategy vector Linking behaviour to GA • This link is the cornerstone of an ING-model

  8. A population of individuals • Genetic variability among individuals • A genotype – phenotype relationship • Individual variation in fitness • Inheritability of genotypes from one generation to the next • Introduction of new genetic variance 500 ind 90 days 34g fat female 303 eggs 0.4 ind 90 days 0.4g fat female 3 eggs Implementing a GA - III Attribute vector

  9. A population of individuals • Genetic variability among individuals • A genotype – phenotype relationship • Individual variation in fitness • Inheritability of genotypes from one generation to the next • Introduction of new genetic variance Implementing a GA - IV + = or Strategy vectors

  10. About fitness (or: who gets to reproduce?) • There are two distinctly different ways to incorporate fitness in an ING-model • By using a fitness measure (applied fitness) • sort all individuals in the population according to the fitness measure and only let the fit ones reproduce. A fitness measure is imposed on the population. Replace the old generation with the new one. No chance of extinction. No population dynamics. • By simulating the individuals’ entire life-span including mortality, gonad development, foraging, metabolic expenditure, etc… (emergent fitness) • individuals will reproduce off-spring according to how well they adapted they are to the environment. Fitness becomes an emergent property of the model. The off-spring is added to the population as juveniles and do not replace existing individuals. Emergent population dynamics. Population may go extinct.

  11. Model constraints • Environment • Physiology • Temperature dependent effects • Stomach limitation • Prey size limitations • Behavioural limitations • …. (this list really never ends)

  12. GA overview

  13. Artificial Neural Network • The basic idea of an ANN was to make an algorithm that mimicked how a brain makes decisions based stimuli A real network of neurons An artificial neural network (ANN) From www.greenspine.ca/media/neuron_culture_800px.jpg

  14. Artificial Neural Network - Architecture • An ANN is constructed of: • Input • Input nodes • Input connection weights • Hidden nodes • Hidden node bias • Output connection weights • Output node(s)

  15. Artificial Neural Network – Input node • An input node receive a specific input and scales it linearly to a value between 0 and 1

  16. Artificial Neural Network – Hidden node • The hidden node sums all input connection weights (CW) multiplied with the input node value

  17. HiddenNodejT HiddenNodej Artificial Neural Network – Transformation • After obtaining the value HiddenNodej the value is transformed non-linearly. Most often a sigmoid function is used. A bias is also often included.

  18. Artificial Neural Network – Output • The output node sums the transformed hidden node values multiplied with the output connection weights

  19. Artificial Neural Network – Behaviour • The value calculated by the output node(s) is used to determine behaviour. This can be done in several ways: • Use value directly (e.g. output = swimming speed) • Use it to determine incremental step in behaviour (e.g. NewDepth = OldDepth + output) • Transform it (sigmoid) and multiply with some maximum range(e.g. NewDepth = MaxDepth*outputT)

  20. ING-models: Pros and cons • Cons • No guarantee that the optimal solution is found • Need to run replicate simulations • Can be difficult to “decode” the adapted neural network ANN = black box? • Pros • Can incorporate very high levels of complexity: • Stochasticity, Intra- and Inter-specific competition • Can be used to study emergent patterns on different levels simultaneously: • Population dynamics, state-dependent behaviour • Can avoid using a measure of fitness by making fitness an emergent property of the model.

  21. Example: A model of a planktivours fishStrand, E., Huse,G., Giske, J. (2002) • Time resolution • Simulates 1 day every month (and scales it to the entire month) • Each day is divided into 5 minutes time-steps • Run for several hundred generations • Behaviour and life-history strategy • Depth position • Energy allocation • Spawning strategy • Emergent fitness • Main focus • Differences in juvenile and adult behaviour • Effects from stochastic juvenile survival on life-history and behaviour

  22. Example: A model of a planktivours fish

  23. Vertical migration From Baliño and Aksnes (1991)

  24. Energy allocation Data from Hamre (1999)

  25. Spawning behaviour

  26. The End

More Related