Week 8 - Multi-Objective EAs and Neural Networks Flashcards
What is domination criterion?
A solution a is said to ‘dominate’ another b in the population if it is at least as good as b in every dimension and better than b in at least one dimension (objective).
What is the pareto-front?
Given the domination criterion, we know that the best solutions will lie along a curve consisting of non-dominated points. This is known as the ‘pareto-optimal front’ or ‘pareto-front’.
Name two desirable characteristics of the pareto-front.
Evenly-spaced solutions and covering the largest possible area of the front.
In multi-objective EAs, where does the best solution lie?
On the pareto-front.
How does an optimal pareto domination tournament work?
Select two random individuals a & b and a separate comparison set c from the population. If a or b is non-dominated with respect to c, then select. If a and b have the same domination – tiebreak.
What is niching?
Separate fitness landscape or genotype into ‘Niches’. Prefer individuals in a niche with less other individuals.
What does NPGA stand for?
Niched Pareto Genetic Algorithm
What does NSGA stand for?
Non-dominated Sorting Genetic Algorithm
What is a neuron?
A system for signal processing and memory. A brain is composed of billions of neurons.
What are the three parts that make up a neuron?
Dendritic tree (receives signals), cell body (processes signals), axon (transmit signals)
Describe how a neuron functions.
A neuron receives electrical activity from other neurons along its dendrites (inputs).
The cell body makes a non-linear decision based on incoming pulses.
The axon will produce a pulse based on the strength of the incoming pulse.
It is then passed to other neurons connected to this one via a synapse.
What is a synapse?
A chemical junction which can be modified and is thought to be where the learning takes place. Synapses can excite or inhibit the dendrite. The synapse can release more neurotransmitter to enhance the coupling between cells.
What did McCulloch and Pitts create in 1943?
The artificial neuron, capable of processing simple logical expressions.
What is Hebbian Learning (1949)?
- When two neurons fire together the connection between neurons is strengthened.
- The activity of firing is one of the fundamental operations necessary for learning and memory.
What did Rosenblatt create in 1962?
The perceptron, which can learn by means of weighted connections.
Name a problem with Rosenblatt’s perceptron?
It could not correctly solve the XOR function.
What did the multi-layer perceptron solve?
The XOR problem a single perceptron could not solve.
Name some applications of neural computing.
Image analysis (e.g. facial recognition)
Autonomous vehicles
Robotics
Data analysis
Classification/prediction
Pattern detection
Control systems in engineering applications (e.g. power stations and other safety critical systems)
Name some variations on the perceptron.
Multi-Layer Perceptrons (including deep neural networks)
Recurrent neural networks
Self-organising maps
Kohonen networks
Boltzmann machines
Probabilistic neural networks
What does MOGA stand for?
Multi Objective Generational Algorithm
How does NPGA work?
Generate initial population of solutions
Select individuals:
1. Run pareto-tournament domination selection. If one solution is non-dominated and another not, go to 3, otherwise go to 2
2. Compute niched fitness to separate solutions
3. Repeat for n selected individuals
Crossover and mutate to generate new population
What is NSGA?
Elitist MOGA, generates new solutions using selection, crossover and mutation. Uses a fast non-dominated sort to rank solutions. Uses crowding distance as a tie breaker.
What is crowding distance?
For each objective m:
1. Sort population by value of m
2. For end solutions crowding distance = infinity
3. For other solutions (i), the crowding distance for objective m = (i+1).m - (i-1).m and add this solution to the sum for i
Repeat for all i and for all m
