Flood decision support system on agent grid: method and implementation
Enterprise Information Systems
Study on the evolutionary optimisation of the topology of network control systems
Enterprise Information Systems
Enterprise Information Systems
A survey of software adaptation in mobile and ubiquitous computing
Enterprise Information Systems
Intelligent bionic genetic algorithm (IB-GA) and its convergence
Expert Systems with Applications: An International Journal
Structure of Multi-Stage Composite Genetic Algorithm (MSC-GA) and its performance
Expert Systems with Applications: An International Journal
Healthcare information systems: data mining methods in the creation of a clinical recommender system
Enterprise Information Systems
Enterprise Information Systems
An Integrated Approach for Agricultural Ecosystem Management
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An inter-temporal resource emergency management model
Computers and Operations Research
Study on solution models and methods for the fuzzy assignment problems
Expert Systems with Applications: An International Journal
Random assignment method based on genetic algorithms and its application in resource allocation
Expert Systems with Applications: An International Journal
Block-matching algorithm based on harmony search optimization for motion estimation
Applied Intelligence
Hi-index | 12.06 |
How to measure the adaptation complexity effectively is an open issue in natural or artificial systems. In this paper, some essential characteristics of adaptation in evolvable systems and the importance/complexity of constructing multi-objective fitness functions in evolutionary computation are analyzed. Based on the authors' previous work on single-objective normalization, a general method is put forward for multi-objective decision making and optimization with its key idea of decomposing the process of constructing fitness functions into their basic units (classes). Then, the issues of determining the corresponding mathematical models and their parameters as well as the issue of integrating all the fitness functions are discussed. Variable weights/objective synthesis is also briefly discussed. A technique in multi-input-multi-output control systems is illustrated to show the usefulness of the method.