Geyer-Schulz et al. athttp://www.cs.cmu.edu/jmount/g3.html)atCarnegieMellon.Inthisapproach many users evaluate the objects bred by the genetic algorithm, the evaluations are collected and aggregated by the fitness function. A new generation of objects is generated as soon as a certain number of evaluations have been collected or a pre- specified period of time has passed. This approach suffers from two draw-backs first. the user receives no immediate feedback and he can not see. how his evalua tions have influenced the outcome of the algorithm and, second, if users have non- homogeneous utility functions and this is the case for heterogeneous user groups the averaging process inherent in the aggregation of the fitness values may lead to art objects whose aesthetic is a disappointment for all users. This effect is even more pronounced with small population sizes The main problems for applying interactive evolutionary algorithms currently ed by 1. The human fitness bottleneck caused by the required explicit evaluation of de- signs by the users 2. The heterogeneity of user groups which leads to non-homogeneous fitness func Bottleneck and Heterogeneous User Group Fitness 3 The Design of my VU: Addressing the humai To address the problems identified in the previous section, the design of my VU is based on the metaphor of an information market(broker) and on the principle of self-assessment of experience. This is an application of the economic principle of self-selection which has been suggested as a rationale for versioning information products by Shapiro and Varian(1999). In my VU we generate recommendations on information products(web-sites) from observed purchasing behavior for infor- mation products. The key idea is identify the information market as a system and the rest of the internet as its environment whenever the user follows an external link from the information market to an information product and crosses the system boundary, this is registered as a purchase incident. Use of internal links in the broker reveals the users' preferences for broker services. Information products in the virtual university have a rich meta-data description with one or more category attributes. In addition, in my VU users have the opportunity to incrementally establish a profile of their experience with each information product category visited(self-assessment of In the following the main loop of the interactive evolutionary algorithm of my VU shown in Figure 3 is explained in more detail. Numbers in parenthesis refer to arcs in Figure 3. In my VU recommendations are usually presented as ranked and labelled ists of suggested information products( web-sites) 1. The user perceives(1)a list of labelled links to other web-sites(e.g. his personal favorites shown in Figure 4)which constitutes an element of the user interface4 Geyer-Schulz et al. at http://www.cs.cmu.edu/˜jmount/g3.html) at Carnegie Mellon. In this approach, many users evaluate the objects bred by the genetic algorithm, the evaluations are collected and aggregated by the fitness function. A new generation of objects is generated as soon as a certain number of evaluations have been collected or a pre￾specified period of time has passed. This approach suffers from two draw-backs: first, the user receives no immediate feedback and he can not see, how his evalua￾tions have influenced the outcome of the algorithm and, second, if users have non￾homogeneous utility functions and this is the case for heterogeneous user groups, the averaging process inherent in the aggregation of the fitness values may lead to art objects whose aesthetic is a disappointment for all users. This effect is even more pronounced with small population sizes. The main problems for applying interactive evolutionary algorithms currently used by artists and engineers for personalized web-design are: 1. The human fitness bottleneck caused by the required explicit evaluation of de￾signs by the users. 2. The heterogeneity of user groups which leads to non-homogeneous fitness func￾tions. 3 The Design of myVU: Addressing the Human Fitness Bottleneck and Heterogeneous User Groups To address the problems identified in the previous section, the design of myVU is based on the metaphor of an information market (broker) and on the principle of self-assessment of experience. This is an application of the economic principle of self-selection which has been suggested as a rationale for versioning information products by Shapiro and Varian (1999). In myVU we generate recommendations on information products (web-sites) from observed purchasing behavior for infor￾mation products. The key idea is identify the information market as a system and the rest of the Internet as its environment. Whenever the user follows an external link from the information market to an information product and crosses the system boundary, this is registered as a purchase incident. Use of internal links in the broker revealsthe users’ preferences for brokerservices. Information products in the virtual university have a rich meta-data description with one or more category attributes. In addition, in myVU users have the opportunity to incrementally establish a profile of their experience with each information product category visited (self-assessment of experience). In the following the main loop of the interactive evolutionary algorithm of myVU shown in Figure 3 is explained in more detail. Numbers in parenthesis refer to arcs in Figure 3. In myVU recommendations are usually presented as ranked and labelled lists of suggested information products (web-sites). 1. The user perceives(1) a list of labelled links to other web-sites (e.g. his personal favorites shown in Figure 4) which constitutes an element of the user interface