a multi-objective bisexual reproduction genetic algorithm for ...

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Alkan et al. have developed a Memetic Algorithms (MAs) by combining GAs
and local search techniques, hill climbing [1]. This approach has achieved good
computational performance. The idea behind hill climbing approach is to create a hill
climbing method for each type of constraint and combine them under a single hill
climbing method, denoted as AHC. Starting from a high resolution, select a constraint
type based hill climbing method by using a selection method, giving a higher chance
to an operator of the related constraint type causing more violations. There are 3
improvement strategies. First of all, invoke the selected operator for the related type
of constraints, producing a new individual. Second, if this attempt does not make any
improvement on the old one, ignore the new individual. Depending on the constraint
type, a selected block of genes, possibly causing more violations among the other
blocks, are attempted to be corrected. Finally, if this attempt also fails to produce a
better individual, then using the old one, a selected single gene in a block of genes,
possibly causing more violations, is attempted to be corrected. If the fitness of an
individual improves in any case, AHC is reapplied on it.
Some other researchers have also used distributed and parallel computing
models for course scheduling problem. One of them is the Multi Agent System model,
which has mentioned to problems that are similar to our study.
The Multi Agent System (MAS) model has been introduced to the course
scheduling problem by Kaplansky et al. [35]. The architecture is composed of a set of
autonomous scheduling agents (SAis) that solve the course scheduling for each
department. Each agent has its own course scheduling problem and its own goals. The
scheduling agents must coordinate these goals with the other agents in order to
achieve a solution for the whole organization that yields a better result with respect to
the global targets. To achieve a coherent and consistent global solution, the SAs make
use of a sophisticated negotiation protocol among scheduling agents that always ends
in an agreement (not ensured to be optimal). The main functionalities of this protocol
are agent to agent relation definition, a mechanism to approve a chain of request for
changes (RfC) and an electronic marketplace for bidding on preferred common timeslots.