1) We have a system for very easily instancing new problems, and have already implemented most baselines such as Fonseca, Srinivas, ConstrEx and more. Capabilities can handle constrained or non-constrained problems.
Modifiable Genetic Algorithm Approach
2) A basis for a very plain and dull GA is as follows:
- Read a 100-row dataset for the problem (so that we begin on a consistent standpoint).
- Evaluate all of these as the initial population and record Median & IQR metrics for each objective.
- Begin an iterative process, stopping at a given criteria (# of runs, or %change between iterations):
- - - a: Select offspring for CM
- - - b: Crossover for the offspring
- - - c: Mutation for the offspring
- - - d: Combine population with the new offspring and select a new population from these
3) Different Algorithms follow from adjusting (a,b,c,d) above:
- RRSL GA:
- - - a: Selection of offspring via rrsl non-dominating leaves. Maintain two copies of these offspring.
- - - b: Crossover of the offspring via moving averages idea
- - - c: Mutation for the offspring via Differential Evolution
- - - d: Tournament Selection on the (few) offspring + adjustedoffspring
4) Benchmark Algorithms also follow from adjusting (a,b,c,d) above:
- - - a: Selection of offspring using k-round tournament selection
- - - b: Standard Crossover to swap attributes for a given pair of individuals
- - - c: Standard Mutation to re-roll the dice on some individual's attribute chosen decision
- - - d: Selection via NSGA2 or SPEA2 sub-routines.
5) The Goal of the Research:
- We want RRSL GA to perform as well as NSGA2/SPEA2.
- There's an argument on what it means to be better. Solutions lie across the optimal pareto frontier, but different algorithms may discover solutions at different areas along the frontier.
- The absolute emphasis of the research: Improving upon the number of objective function evaluations. The theory is that RRSL GA will require log N the number of evaluations needed by NSGA2/SPEA, and still do just as well.
--- full results pending ---