Skip to content
GitLab
Menu
Projects
Groups
Snippets
/
Help
Help
Support
Community forum
Keyboard shortcuts
?
Submit feedback
Contribute to GitLab
Sign in / Register
Toggle navigation
Menu
Open sidebar
ROCHEL Axel
sho
Commits
045d4b9a
Commit
045d4b9a
authored
Jan 06, 2020
by
Johann Dreo
Browse files
Lesson summary/syllabus.
parent
5aee3fb3
Changes
1
Hide whitespace changes
Inline
Sidebyside
LESSON.md
0 → 100644
View file @
045d4b9a
Metaheuristics (IA308)
=======================
Introduction

Metaheuristics are mathematical optimization algorithms solving
`$\argmin_{x \in X} f(x)$`
(or argmax).
Synonyms:

search heuristics,

evolutionary algorithms,

stochastic local search.
The general approach is to only look at the solutions, by trial and error, without further information on its structure.
Hence the problem is often labelled as "blackbox".
Link to NPhardness/curse of dimensionality: easy to evaluate, hard to solve.
Easy to evaluate = fast, but not as fast as the algorithm itself.
Hard to solve, but not impossible.
Algorithmics

Those algorithms are randomized and iteratives (hence stochastics) and manipulates a sample (synonym population)
of solutions (s. individual) to the problem, each one being associated with its quality (s. cost, fitness).
Thus, algorithms have a main loop, and articulate functions that manipulates the sample (called "operators").
Main design problem: exploitation/exploration compromise (s. intensification/diversification).
Main design goal: raise the abstraction level.
Main design tools: learning (s. memory) + heuristics (s. bias).
Forget metaphors and use mathematical descriptions.
Seek a compromise between complexity, performances and explainability.
The is no better "method".
Difference between model and instance, for problem and algorithm.
No Free Lunch Theorem.
But there is a "better algorithm instances on a given problem instances set".
The better you understand it, the better the algorithm will be.
Problem modelization

Way to assess the quality: fitness function.
Way to model a solution: encoding.
### Main models
Encoding:

continuous (s. numeric),

discrete metric (integers),

combinatorial (graph, permutation).
Fitness:

monoobjective,

multimodal,

multiobjectives.
### Constraints management
Main constraints management tools for operators:

penalization,

reparation,

generation.
Performance evaluation

### What is performance
Main performances axis:

time,

quality,

probability.
Additional performance axis:

robustness,

stability.
Golden rule: the output of a metaheuristic is a distribution, not a solution.
### Empirical evaluation
Proofreality gap is huge, thus empirical performance evaluation is gold standard.
Empirical evaluation = scientific method.
Basic rules of thumb:

randomized algorithms => repetition of runs,

sensitivity to parameters => design of experiments,

use statistical tools,

design experiments to answer a single question,

test one thing at a time.
### Useful statistical tools
Statistical tests.

classical null hypothesis: test equality of distributions.

beware of pvalue.
How many runs?

not always "as many as possible",

maybe "as many as needed",

generally: 15 (min for nonparametric tests)  20 (min for parametricgaussian tests).
Use robust estimators: median instead of mean, Inter Quartile Range instead of standard deviation.
### Expected Empirical Cumulative Distribution Functions
On Run Time: ERTECDF.
```
$ERTECDF(\{X_0,\dots,X_i,\dots,X_r\}, \delta, f, t) := \#\{x_t \in X_t  f(x_t^*)>=\delta \}$
$\delta \in [0, max_{x \in \mathcal{X}}(f(x))]$
$X_i := \{\{ x_0^0, \dots, x_i^j, \dots, x_p^u  p\in[1,\infty[ \}  u \in [0,\infty[ \} \in \mathcal{X}$
```
with $p$ the sample size, $r$ the number of runs, $u$ the nubmer of iterations, $t$ the number of calls to the objective
function.
The number of calls to the objective function is a good estimator of time because it dominates all other times.
The dual of the ERTECDF can be easily computed for quality (EQTECDF).
3D ERT/EQTECDF may be useful for terminal comparison.
### Other tools
Convergence curves: do not forget the golden rule and show distributions:

quantile boxes,

violin plots,

histograms.
Algorithm Design

### Neighborhood
Convergence definition(s).

strong,

weak.
Neighborhood: subset of solutions atteinable after an atomic transformation:

ergodicity,

quasiergodicity.
### Structure of problem/algorithms
Structure of problems to exploit:

locality (basin of attraction),

separability,

gradient,

funnels.
Structure with which to capture those structures:

implicit,

explicit,

direct.
Silver rule: choose the algorithmic template that adhere the most to the problem model.

taking constraints into account,

iterate between problem/algorithm models.
### Grammar of algorithms
Parameter setting < tuning < control.
Portfolio approaches.
Example: numeric low dimensions => NelderMead Search is sufficient.
Algorithm selection.
Algorithms are templates in which operators are interchangeable.
Most generic way of thinking about algorithms: grammarbased algorithm selection with parameters.
Example: modular CMAES.
Parameter setting tools:

ParamILS,

SPO,

irace.
Design tools:

ParadisEO.
### Landscapeaware algorithms
Fitness landscapes: structure of problems as seen by an algorithm.
Features: tool that measure one aspect of a fitness landscape.
We can observe landscapes, and learn which algorithm instance solves it better.
Examples: SAT, TSP, BB.
Toward automated solver design.
Write
Preview
Supports
Markdown
0%
Try again
or
attach a new file
.
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment