Multiobjective Evolutionary Optimization of DNA Sequences for Reliable DNA Computing

Evolutionary Computation & DNA Computing

Evolutionary Algorithms for Multiobjective Optimization

Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separat...

Evolutionary Multiobjective Optimization

Very often real world applications have several multiple conflicting objectives. Recently there has been a growing interest in evolutionary multiobjective optimization algorithms which combines two major disciplines: evolutionary computation and the theoretical frameworks of multicriteria decision making. In this introductory chapter, we define some fundemental concepts of multiobjective optimi...

Editorial: Evolutionary Multiobjective Optimization

Multiobjective optimization is about finding solutions to problems with respect to multiple, often conflicting, decision criteria. Also termed multicriteria optimization or vector optimization, this area has been strongly developed in the 1970s within operations research, decision theory [1], and engineering [17]. Typical application examples range from expected profit versus risk in portfolio ...

Inferring Evolutionary History from DNA Sequences

We are iiit,erested here in t w o related problems. The first is determining whether we can triangulate a vertex-colored graph without introducing edges between vertices of the same color. This is related to a fundamental problem for geneticists, that of using charact,er state information to construct evolutionary trees. lye demonstrate the polynomial equivalence of these problems. .4n importan...