الفهرس 
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Abstract
Optimization problem involving both continuous and discrete variables
are able to describe many real world problems. A mixed variable pro-
gramming problem is an optimization problem refers to mathematical
programming with continuous variables and discrete variables (zero-
one variables, integer variables or discrete variables). A particular
class of such problems is called mixed variable programming which is
important but di cult to solve. Di erent type of problems in eco-
nomics, science, engineering, tra c and medicine can be reformulated
as a mixed variable programming problem. So, in this work, we pay a
great attention to solve this problem.
Global optimization problems represent a main category of such
problems. Global optimization refers to finding the extreme value of a
given non-convex function in a certain feasible region. Such problems
are classified in two classes; unconstrained and constrained problems.
In this study, both global optimization problem classes; uncon-
strained and constrained problems are considered. New hybrid ver-
sions of genetic algorithm are proposed as promising solver for the
considered problems. The proposed methods aim to overcome the
drawbacks of slow convergence and random constructions of genetic
Approval
algorithm. In this hybrid metho ds, local search strategies are laid in-
side genetic algorithm in order to guide them, especially, in the vicinity
of local minima, and overcome their slow convergence, especially, in
the final stage of the search.
Data clustering is related to many disciplines and plays an important
role in a wide range of applications. The applications of data clustering
usually deal with large datasets and data with many attributes. Data
clustering has found many applications, including do cument extrac-tion,
image segmentation, market research, social network analysis,
etc. Data clustering problem can be reformulated as a mixed variable
programming. In order to examine the designed methodologies in this
study, they are applied to solve the data clustering problem.