PRACTICALITIES CITS4404 Artificial Intelligence & Adaptive Systems

PRACTICALITIESCITS4404Artificial Intelligence & Adaptive Systems

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Issues in applying CI techniques• Global optimisation, some definitions • Fitness progression • Generality • Role of domain knowledge • Niching and speciation • Memetic optimisation • Multi-objective problems • Co-evolution • Constraints • Noisy and dynamic problems • Offline vs online learning • Supervised, reinforcement, unsupervised learning • Experimental methodology, performance measures • Parameter tuning and/or control

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Global optimisation• Given a function f and a set of solutions S, search for

x* S such that x S: f(x*) beats f(x) • The graph below depicts a simple 1D fitness landscape

http://www.cs.vu.nl/~gusz/ecbook/ecbook-course.html

x* Global optimum

Local optima

Three basins of attraction

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Definitions • The function f giving the fitness for each member of S is

called the fitness landscape • The best solution wrt f is called the global optimum

• Note that there may be multiple (equal) global optima

• Non-global optima that are better than other “similar solutions” are called local optima

• The part of the landscape dominated by an optimum is called its basin of attraction

• Diversity refers to the distribution of a set of solutions over the fitness landscape • Diversity preservation techniques try to ensure a good distribution

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Heuristic optimisation• CI techniques are heuristic optimisers, also known as

generate-and-test optimisers • Searching procedures that use rules (inspired by nature) to decide

which solution(s) to try next

• The simplest heuristic optimisers are hill-climbers • Given one solution, generate similar solutions, and

keep the best of them

• A hill-climber is guaranteed to find a local optimum • They can exploit their basin of attraction, but they lack the ability to

explore the entire landscape properly

• CI techniques use populations and other tricks to promote both exploration and exploitation • More about this later under memetic algorithms

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Fitness progression• CI techniques are anytime algorithms • The fitness of the best known solution will improve over time,

but usually under the law of diminishing returns • The convergence rate (the rate of improvement) tends to fall over time

• Several shorter runs may be better than one long run

Progress in 1st half

Bes

t fit

ness

in p

opul

atio

n

Time (number of generations)

Progress in 2nd half

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Smart initialisation• Initial solutions can be generated randomly, or using

domain knowledge, often called smart initialisation • This can improve both results and time performance,

but it may also introduce bias into the search

Bes

t fit

ness

in p

opul

atio

n

Time (number of generations)

T: time needed to reach equivalent fitness of “smart” initialisation

T

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CI vs problem-specific methods • CI techniques are general-purpose

• Robust techniques giving good performance over a range of problems

Scale of “all” problems

Per

form

ance

Random search

Special problem-tailored method

Computational intelligence approach

P

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Domain knowledge • Performance can sometimes be improved by incorporating

“expertise” into the process

Scale of “all” problems

Per

form

ance

P

EA 1

EA 4

EA 3

EA 2

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Contd. • Too little domain knowledge makes the search space

bigger and can make the search inefficient • cf. EA1

• Too much domain knowledge can exclude novel solutions• cf. EA4

• But care must be taken!• “If you tell the system what the solution looks like,

that’s what it’ll give you!” [R.L. While]

• But this can all be highly non-obvious…

• Most interesting problems are multi-modal • Sometimes we want to discover more than just the global optimum• i.e. we want to discover y* and z*, as well as x*

• This might be important to offer extra robustness • Often it is hard for a fitness function to capture everything

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Niching and speciation

x*

y* z*

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Contd.• Each basin of attraction is called a niche or sometimes

a species • Niching can be achieved in two broad ways• Implicit niching is achieved by modifying the solution

representation • Explicit niching is achieved by promoting dissimilar solutions,

or penalising similar solutions• Both techniques rely on having some distance metric

between solutions

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Memetic algorithms• CI techniques are good at exploration – finding high peaks

in the fitness landscape – but are less good at exploitation of those peaks

• Memetic algorithms combine CI with some local-search technique that is good at exploitation • AKA Baldwinian, Lamarckian, or cultural algorithms

• Hill-climbing is the classic example • CI finds the best basin of attraction, then hill-climbing climbs the peak

• The two techniques can be applied in series, or in parallel

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Multi-objective optimisation • In many problems, solutions are assessed wrt several criteria

• e.g. speed vs safety vs price for car designs

• Fitness is now a vector, not a scalar, which complicates selection • Vectors have only a partial ordering, rather than a total ordering

• The “solution” to a multi-objective problem is a set of solutions offering different trade-offs between the objectives • It is important to make no a priori assumptions about trade-off weights

or the shape of the solution set

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Contd. Rank = 0

Rank = 1

Rank = 2

Rank = 1

Rank = 4

f2

f1

PQ

A

B

A and B are non-dominated

Rank = 0

Rank = 0

Rank = 0

P dominates Q

Two objectives f1 and f2, both being maximised

X dominates Y if it is better in all objectives

The rank of X is the number of solutions that dominate X

Selection is based on ranks

Each solution is plotted by its values in the objectives

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Co-evolution• Fitness is sometimes assessed by the interactions

between solutions, rather than in isolation • e.g. build a team which is the best in the AFL

• The above is an example of competitive co-evolution • Aim for an “arms race” between solutions to drive improvement

through parallel adaptation

• The alternative is co-operative co-evolution • Decompose a problem into simpler sub-tasks (layered learning),

and combine the sub-solutions to solve the original problem

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Constraints • A constraint is a requirement placed on a solution,

as opposed to a measure of quality • e.g. the length of this component must be less than X,

or the power of that component must be more than Y

• A feasible solution is one that satisfies all constraints• An infeasible solution fails at least one constraint

All solutions

Feasible

Feasible

Feasible

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Contd. • There are many different constraint-handling techniques

• Separatist: consider objectives and constraints separately• Purist: discard all infeasible solutions when they arise • Repair: repair infeasible solutions when they arise • Penalty: modify the fitness function to penalise infeasible

solutions • MOOP: add an extra objective function that measures

“degree of infeasibility” • Hybrid: some combination of the above

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Noisy problems • A noisy fitness function arises when the fitness calculations

aren’t perfect

Fitness landscape Noise landscape Noisy fitness landscape

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Contd. • The algorithm can now only estimate performance • Again, this complicates selection

• Bad solutions might get lucky and survive • Good solutions might get unlucky and die

• These behaviours cause undesirable long-term effects • The learning rate is reduced • Learning may not be retained

• The usual approach to this is resampling • Evaluate the fitness multiple times and average the results • But how many times is sufficient?

• A second common approach is to try to bound the error • Basically to assume the error won’t exceed a certain magnitude

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Dynamic problems• With some problems, the

fitness landscape changes over time, maybe due to • Temporal effects • External factors • System adaptation

• The system needs to adapt to this change • Requires online learning

http://www.natural-selection.com

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Offline vs online learning • Offline learning is where a system learns before use

• The strategy is fixed once training is completed • Requires comprehensive training data • Only feasible in well-understood environments

• Online learning is where a system learns while in use • The strategy is adapted from each instance encountered • Initial decisions are made from incomplete training data • Usually much greater time-pressure to improve

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Learning paradigms • Supervised learning is (offline) training by

comparing a system’s responses to expected responses in training data

• Reinforcement learning is (online) training using feedback from the environment to assess the quality of responses

• Unsupervised learning is (offline) training with no training data • No real question presented • The system looks for patterns in the data

• Question: tell me about this data

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Unsupervised learning • Question: tell me about this data

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Supervised learning • Question: what is an apple?

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Experimental methodology • CI techniques are stochastic

• Their results are non-deterministic

• Thus we should never draw conclusions from a single run• Always perform a “large” number of runs • Assess results using statistical measures • Assess significance using statistical tests

• When comparing algorithms, it is crucial to make all comparisons fair • Give each the same amount of resource • Use the same performance measures • Try different competition limits

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Performance measures • Offline performance measures

• Effectiveness (algorithm quality) • Success rate (percentage of “good’ runs) • Mean best fitness at termination

• Efficiency (algorithm speed) • CPU to “completion” • Number of solutions evaluated

• Online performance measures • Population distribution • Fitness distribution • Improvement rate

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Parameter tuning • How do we decide on the various constants in a run

of the system? • e.g. bigger population, or more generations? • How big should mutations be?

• This can be difficult! • Sub-optimal values can seriously degrade performance • Choosing good values can take significant time • Exhaustive search is usually impractical • Good values may become bad during a run

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Parameter control • Can we get the system to choose parameters automatically?

• i.e. allow settings to vary during the run

• Three main alternatives are used • Deterministic: change parameters according to some pre-determined

schedule, e.g. based on the passage of time • Adaptive: change parameters according to some measure of the

search progress • Self-adaptive: encode the “scope of change” into the solution

representation in some way

• One important goal is to reduce the prevalence of “magic numbers” in the system • Still, finding good settings is not easy