We have already proposed a multi-objective genetic local search algorithm for finding nondominated solutions of multi-objective optimization problems (Ishibuchi & Murata 1998). In our hybrid algorithm, a local search procedure is applied to each solution generated by genetic operations (i.e., selection, crossover, and mutation). Since our optimization problem involves multiple objectives, the a...

This paper investigates the use of partial functions and fitness sharing in genetic programming. Fitness sharing is applied to populations of either partial or total functions and the results compared. Applications to two classes of problem are investigated: learning multiplexer definitions, and learning (recursive) list membership functions. In both cases, fitness sharing approaches outperform...

This thesis explores genetic algorithm and rule-based optimization techniques used by cognitive radios to make operating parameter decisions. Cognitive radios take advantage of intelligent control methods by using sensed information to determine the optimal set of transmission parameters for a given situation. We have chosen to explore and compare two control methods. A biologically-inspired ge...

In the presence of imprecise management targets, staff preferences, and patients’ expectations, the healthcare staff scheduling problem becomes complicated. The goals, preferences, and client expectations, being humanistic, are often imprecise and always evolving over time. We present a fuzzy genetic algorithm (FGA) approach for addressing healthcare staff scheduling problems in fuzzy environme...

In practice, obtaining the global optimum for the economic dispatch {bf (ED)}problem with ramp rate limits and prohibited operating zones is presents difficulties. This paper presents a new andefficient method for solving the economic dispatch problem with non-smooth cost functions using aFuzzy Adaptive Genetic Algorithm (FAGA). The proposed algorithm  deals  with the issue ofcontrolling the ex...

The weekly maintenance schedule specifies when maintenance activities should be performed on the equipment, taking into account the availability of workers and maintenance bays, and other operational constraints. The current approach to generating this schedule is labour intensive and requires coordination between the maintenance schedulers and operations staff to minimise its impact on the ope...

How does one optimize a tness function when the values it generates have a stochastic component How does one simultaneously optimize mul tiple tness criteria These questions are important for many applications of evolutionary computation in an experimental environment Solutions to these problems are presented along with discussion of situations where they arise such as modeling and genetic prog...

This paper describes a technique that extends the unimodal particle swarm optimizer to efficiently locate multiple optimal solutions in multimodal problems. Multiple subswarms are grown from an initial particle swarm by monitoring the fitness of individual particles. Experimental results show that the proposed algorithm can successfully locate all maxima on a small set of test functions during ...

We present experiments in which a group of autonomous mobile robots learn to perform fundamental sensor-motor tasks through a collaborative learning process. Behavioural strategies (i.e. motor responses to sensory stimuli) are encoded by means of genetic strings stored on the individual robots, and adapted through a genetic algorithm executed by the entire robot collective: robots communicate t...