WORCESTER POLYTECHNIC INSTITUTE
Computer Science Department
CS4341 ❏ Artificial Intelligence
Sun Apr 21 17:31:05 EDT 2013
PROJECT 3 - Finding the Best Search Team
All projects will be graded out of 100 points for convenience, and will
be adjusted later to conform to the class grading scheme already provided
to you. Grading will be subtractive. That is, you start with 100 points,
and points will be deducted for problems found. This produces lower
scores, is harder to grade, but is much fairer and more consistent.
The grading will be divided into consideration of:
* 10 pts Presentation (i.e., style, layout, comments)
* 30 pts Required (i.e., what the problem description asked for)
* 60 pts Demonstration (i.e., the output from and actions by the system
-- layout, clarity, completeness, how well tested).
Clear documentation and descriptions.
Good coding standards.
Clear system output.
Readable tests and explanations.
- uses GA technique including mutation and crossover
- uses rank-space method (quality rank and diversity rank)
- uses team representation given in problem (or similar)
- uses environment provided
- starts with one team
- starts each new population with 4 teams
- does all possible crossovers between the current teams
- applies mutation to all as well as crossover
- doesnt allow duplicate teams in a population
- evaluates team fitness using 5 pseudo-random placements,
as well as the seeing/hearing approximation
- have provided items:
# Brief, clear documentation that describes the design of your
GA system. What special algorithms or data structures were used?
# A description of, and rationale for, your biasing method.
# A description of, and rationale for, your team diversity measure.
# A description of, and rationale for, your stopping condition.
# The code for the system (which must be well commented).
# The clear, readable output from the test/demonstration runs.
(These should not be annotated).
# Output in "demonstration" format.
# Output of condensed team representation for each generation.
All aspects working & correct.
-- teams formed according to rules
-- mutation & crossover as prescribed
-- duplicate teams discarded
-- pseudo-random placement
-- seeing/hearing algorithm
-- team quality calculations
-- team diversity calculation
-- rank-space method
-- stopping condition
-- best team found
How well tested (look for variations due to randomness).
-- include case of the landscape provided.
-- include one or more "special cases" that have been
used to convince yourself that the system is working.
Completeness of messages in "demonstration" output.
(i.e., we understand what's happening)
(i.e., it is an explanation, not a trace)
(N.B., for this project this is very important!)
Include an explanation of why you think that the team found by
the GA is the best.