Evolutionary Algorithms: Handling Constraints and Real-World Application. 

Abstract: 

    The present work is a heuristic and experimental  study in the evolutionary 
computation domain,  and starts with  an introduction to the artificial evolution 
with a synthesis of the principal approaches. 
  
The first part is a heuristic study devoted to constraint handling in evolutionary 
computation. It presents  an extensive review of previous constraint handling 
methods in the literature and their limitations. Two solutions are then proposed. 
The first idea is to improve genetic operator  exploration capacity for constrained 
optimization problems. The logarithmic mutation operator is conceived to explore 
both locally and globally the search space. 
The second solution introduces  the original Adaptive Segregational Constraint 
Handling Evolutionary Algorithm (ASCHEA), the main idea of which is to maintain 
population diversity. In order to achieve this goal, three main ingredients are used: 
An original adaptive penalty method, a constraint-driven recombination, 
and a segregational selection that distinguishes between feasible and infeasible 
individuals to enhance the chances of survival of the feasible ones. Moreover, 
a niching method with an adaptive radius is added to ASCHEA in order to handle 
multimodal functions. 
Finally, to complete the ASCHEA system, a new equality constraint handling 
strategy is introduced, that reduces progressively the feasible domain in order 
to approach the actual null-measured domain  as close as possible  at the 
end of the evolution.