Acquired Intelligence & Adaptive Behaviour Week 3 - 3
Overview
1. What is evolution
2. Evolutionary Algorithms
3. Related terminology and concepts
- Fitness function
- Genotype and phenotype
4. Artificial Evolution vs Design
5. Examples
*References
- blog
- An Introduction to Genetic Algoirthms - Melanie Mitchell
1. Evolution
- For evolution to occur, you need a population of individuals that:
- has variation
- undergoes selection
- reproduces with heredity
1) Artificial Evolution
The main idea of artificail evolution is to simulate the imortant parts of evolution, to 'harness the creative power of evolution'. To get the computer to build such things, we have to build things beyond design. -> Intelligent behaviour rather than intelligent solutions
2) Evolution -> Evolutionary Algorithm
- In natural evolution, the accumulation of beneficial mutations can reproduce better adapted organisms.
- An evolutionary algorithm (EA), also known as a genetic algorithm (GA) is an attempt to capture this 'improvement of a system' that is the result of natural evolution.
3) Evolutionary Algorithm
- Simulates a population of 'individuals' that:
- reproduce with heritability
- are mutated to produce variation
- are selected on some measure of fitness
*Example:
- Evolution (directed accumulatio nof blind variation) is quite different form rational desgin.
- Human desgin tends to break systems down into components. These are themselves then broken down into subcomponents etc., until basic components are understood. (e.g. modern aeroplane or space craft design)
(1) Advantages
- They introduce different biases than those in place in typical human design.
- Allow us to consider alternative ways to 'solved a problem', other than how a human might have.
- Can be used to automate design:
- Engine design
- Scheduling problems
- Container load optimisation
(2) Different Biases
- Unlike typical human design, GAs are not biased against 'cheap tricks' - i.e. non-intuitive short-cuts to solving problems for hierarchical desgin.
- But they have their own biases.
- There must be an evolutioanry path to any solution given by a GA. (Consider how a bacterium could not evolve in one step (generation) into an elephant.)
- The biases of GAs are not easy to identify. They are influenced by how the genotype is related to the phenotype, how mutation takes place - lots of different, subtle things.
*Engine Design example:
(3) Genotype / Phenotype
- Genotype: the genetic materail, passed down from one generation to the next the order of the nucleotide on DNA, e.g. AGGCACATT...
- Phenotype: what undergoes selection, 'what the genotype codes for'. Typically the body and the brain in artificial evolution.
(a) In Engineering
- Genotype: (the encoding of the information) the angle of each aerofoil. E.g. g0 = [a1 a2 a3 a4 ... a8]
- Phenotype: (what the information encodes) the shape of the wing
(4) Fitness
- In the natural world, the fitness of an organism is determined by many complex interacting factors: predator, prey coevolution, sexual selection, competition with ocnspecifics, luck, etc.
- But evolution =/= evolutionary algorithms. In GAs, there is often a clearly deifned 'fitness function' guidign evolution towards a predetermined objective.
*Fitness in the aerofoil model is the amount of drag on the wing due the airflow.
(5) Evolutionary Strategy
*pseudo code:
