Analyzing Evolutionary Algorithms: The Computer Science Perspective | SpringerLink Skip to
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We present four abstract evolutionary algorithms for multi-objective optimization and
theoretical results that characterize their convergence behavior. Thanks to these results it is …

The theory of evolutionary computation has been enhanced rapidly during the last decade.
This survey is the attempt to summarize the results regarding the limit and finite time …

In the binary evolutionary optimization framework, two mutation operators are theoretically
investigated. For both the standard mutation, in which all bits are flipped independently with …

The inverse problem of groundwater flow is treated with an automatic method that can
produce several alternative solutions at once. During their joint optimization, these solutions …

Solving binary-real problems with bio-inspired algorithms is an active research matter.
However, the efficiency of the employed algorithm varies drastically by tailoring the …

Many real-world problems could be formulated in a way to fit the necessary form for discrete
optimization. Discrete optimization problems could be solved by numerous different …

Many systems architecture optimization problems are characterized by a variable number of
optimization variables. Many classical optimization algorithms are not suitable for such …

When evolutionary algorithms are used for function optimization, they perform a heuristic
search that is influenced by many parameters. Here, the choice of the mutation probability is …

Abstract Evolutionary Multi-objective Optimization (EMO) is becoming a hot research area
and quite a few aspects of Multi-objective Evolutionary algorithms (MOEAs) have been …