When an optimization problem is encoded using genetic algorithms, one must address issues of population size, crossover and mutation operators and probabilities, stopping criteria...
As an immune inspired algorithm, the Dendritic Cell Algorithm (DCA) has been applied to a range of problems, particularly in the area of intrusion detection. Ideally, the intrusio...
In this paper, we rigorously analyse how the magnitude and frequency of change may affect the performance of the algorithm (1+1) EAdyn on a set of artificially designed pseudo-Bo...
Solving complex, real-world problems with genetic programming (GP) can require extensive computing resources. However, the highly parallel nature of GP facilitates using a large n...
The bounded diameter minimum spanning tree problem is an NP-hard combinatorial optimization problem arising, for example, in network design when quality of service is of concern. ...
It is a hard problem to understand the search process of particle swarm optimization over high-dimensional domain. The visualization depicts the total search process and then it w...
Genetic algorithms are often applied to combinatorial optimization problems, the most popular one probably being the traveling salesperson problem. In contrast to permutations use...
Software testing can be re-formulated as a search problem, hence search algorithms (e.g., Genetic Algorithms) can be used to tackle it. Most of the research so far has been of emp...
Hyper-heuristics can be thought of as “heuristics to choose heuristics”. They are concerned with adaptively finding solution methods, rather than directly producing a solutio...
We describe a methodology for evolving Java bytecode, enabling the evolution of extant, unrestricted Java programs, or programs in other languages that compile to Java bytecode. B...