Evolution manager

The most important issue in the model of GA is how to preserve good genes for generating the better and more effective offspring. The common procedure is to select the top agents as parent generations to breed new individuals by mixing their genes to replace the eliminated agents

This method is reasonable and effective, but not suitable in our model of

optimizing the evolution by the personal subjective evaluation. The human fluctuation is an important problem in the system based on IEC, and results in evaluating unfair in every round. In other words, the criterion of the user’s evaluation is instable in different rounds. The outstanding agents in previous rounds probably get low grades because of the human fluctuation, and this unexpected failure will cause the good ones to be discarded. Furthermore, there would be an error when agents pick up the musical items by their “intuition”. That is to say the recommended musical items sometimes are not enough to stand for the agent’s judgment of good taste. For this reason, the problem of discarding the wrong agents will be enlarged in our model.

fame value. The agents get a fame value according to the previous behavior. The higher fame values are, the more possible agents survive. Fig.4 shows the example of the status after the users’ evaluation in the round, and then will run the GA selection procedure which picks up the preserved parent generations as well as the discarded agents. As shown in Fig.4 , each agent owns two kinds of attributes; namely, the agent fame value and the fitness values of this round. Selection Method in the system

determines which agents would be discarded or recombined according to the result of weighted computing agents fame value and local grades of this round. After the selection, the local grades of this round also would be merged into the agent fame value for usage of next round. In addition, the history ratio parameter can affect the computation in the selection box. The function of History ratio is to lay particular stress on agents’ fame value or local grades of the round. We can modulate history ratio to adapt the scoring habits of different user. For example, history ratio could be raised if the user’s evaluation is fair and precise.

Figure 4.Selection Box

To determine the time of stopping the evolution and system converging is an important issue in the systems based on GA. Generally speaking, the methods of determining the time are to observe whether the system learning curve has ceased moving or the result of evolution has achieved the expected objective. However, as we have described, we make use of the human beings instead of the fitness function to solve the problem involving subjectivity without criterion. It is for sure that there is no way to define the criterion of judging Art and relatively, there is not an impartial solution to verify our system has converged or the agents have been trained

completely, either. Therefore we propose another solution of determining the converged time by using the agent fame value as before. Just as the public

behavior. Usually the human would become a consultant if he/she holds good fame for a long time. We take advantage of this concept to our system for the usage of determining the converging time. In our system, each agent’s fame value varies in every round, and the system will monitor the agent population to find which agent usually maintains high fame values during a period. If the agent of high fame values with good behavior can past the examination of the time threshold, this agent will be allowed to enter the V.I.P pool. The agents in this pool would not evolve but still keep sharing the genes with the others agents in the circle of evolution. When gathering enough stable agents in the V.I.P pool, the system will terminate the evolution and take these stable agents as the final population for recommendation.

In the process of fundamental GA, the genes of agents with good behavior sometimes would be broken because of ongoing crossover and mutation. That is to say some agents had already missed the most proper timing of stopping the evolution.

So selecting the agents with good behavior can preserve the good genes of the agents in the evolution process and avoid the good structure of genes from being destroyed in the overly evolution.

Besides, this procedure can make the agent population varied. There is always a direct answer for the GA questions and leads to the similar agents population when system converged. But in our case, we hope to train the agents with various styles in order to fit the user’s taste. For this reason, this system adopts the procedure as described before to collect agents, and then “the last survivors” in the final round can recommend multiple kinds of musical items.