Geyer-Schulz et al. one-to-one marketing as described in Kelly(1998)to a considerable extent. The evolutionary algorithm described above is common to all my VU recommender ser vices. All my VU recommender services available today(March 2000)are presented in section 4 together with a first analysis of their usage. Finally, we describe possible future improvements of the system 2 Interactive Evolutionary Algorithms for design In the opening lecture of the 1997 Genetic Programming Conference John Koza identified in his outlook on the future of evolutionary algorithms web-site design as one of the most promising(commercial) application areas. However, not much has been achieved in this area in the last three years. To be honest, we must be a little bit more precise. Interactive evolutionary and genetic algorithms have flourished in mu- sicandartsSeee.g.theweb-siteofCraigReynolds(http://www.red.com/cwr/evolve.html for examples on the www An evolutionary algorithm is characterized by a select-and-mutate approach with a population size of 1, a genetic algorithm in addition by a crossover opera- tor and a population size larger than 1. In this paper, however, we will neglect these differences. The reader may think of an evolutionary algorithm as a border line vari- ant of a genetic algorithm with zero probability of crossover and a population size 3 A survey of applications of interactive evolutionary and genetic algorithms for erspective is presented in Takagi(1996a)an akagi(1996b). However, we are not aware of any application of interactive evo- lutionary or genetic algorithms in the area of web-site personalization for e.g. cus- tomer relationship management and one-to-one marketing. Why Before we answer this question, we give a short review of the two generations of nteractive evolutionary and genetic algorithms used today by artists, musicians and engineers. The main feature of the first generation of these algorithms is that the user of such an algorithm is required to explicitely and directly assign a fitness value to each of the objects bred by the algorithm as shown in Figure 1. The ancestor of these algorithms is Richard Dawkins"Blind Watchmaker"which bred simple tree-based forms-so called biomorphs- with an interactive select-and-mutate approach. See Dawkins(1986). Smith's(1991)interactive genetic algorithm for breeding bug like creatures and Caldwell and Johnston's(1991)approach of assisting a witness in building a facial composite of a criminal suspect with an interactive genetic algo- rithm are two additional representatives of this class of algorithms. Combined with a virtual reality environment and bio-sensors as proposed by Hafner and RoBler (1995)interactive evolutionary al gorithms serve as advanced industrial design envi ronments The class of interactive evolutionary algorithms requires that we can phrase the problem as a search through some parameter space, that we can generate new can- didate solutions in near real-time and that the utility of those candidate solutions can easily be compared by humans, but not by means of a precisely defined fitness2 Geyer-Schulz et al. one-to-one marketing as described in Kelly (1998) to a considerable extent. The evolutionary algorithm described above is common to all myVU recommender ser￾vices. All myVU recommender services available today (March 2000) are presented in section 4 together with a first analysis of their usage. Finally, we describe possible future improvements of the system. 2 Interactive Evolutionary Algorithms for Design In the opening lecture of the 1997 Genetic Programming Conference John Koza identified in his outlook on the future of evolutionary algorithms web-site design as one of the most promising (commercial) application areas. However, not much has been achieved in this area in the last three years. To be honest, we must be a little bit more precise. Interactive evolutionary and genetic algorithms have flourished in mu￾sic and arts. See e.g. the web-site of Craig Reynolds(http://www.red.com/cwr/evolve.html) for examples on the WWW. An evolutionary algorithm is characterized by a select-and-mutate approach with a population size of ✁ , a genetic algorithm in addition by a crossover opera￾tor and a population size larger than ✁ . In this paper, however, we will neglect these differences. The reader may think of an evolutionary algorithm as a border line vari￾ant of a genetic algorithm with zero probability of crossover and a population size of 1. A survey of applications of interactive evolutionary and genetic algorithms for system design from an engineering perspective is presented in Takagi (1996a) and Takagi (1996b). However, we are not aware of any application of interactive evo￾lutionary or genetic algorithms in the area of web-site personalization for e.g. cus￾tomer relationship management and one-to-one marketing. Why? Before we answer this question, we give a short review of the two generations of interactive evolutionary and genetic algorithms used today by artists, musicians and engineers. The main feature of the first generation of these algorithms is that the user of such an algorithm is required to explicitely and directly assign a fitness value to each of the objects bred by the algorithm as shown in Figure 1. The ancestor of these algorithms is Richard Dawkins’ “Blind Watchmaker” which bred simple tree-based forms – so called biomorphs – with an interactive select-and-mutate approach. See Dawkins (1986). Smith’s (1991) interactive genetic algorithm for breeding bug like creatures and Caldwell and Johnston’s (1991) approach of assisting a witness in building a facial composite of a criminal suspect with an interactive genetic algo￾rithm are two additional representatives of this class of algorithms. Combined with a virtual reality environment and bio-sensors as proposed by Hafner ¨ and Roßler ¨ (1995) interactive evolutionary algorithms serve as advanced industrial design envi￾ronments. The class of interactive evolutionary algorithms requires that we can phrase the problem as a search through some parameter space, that we can generate new can￾didate solutions in near real-time and that the utility of those candidate solutions can easily be compared by humans, but not by means of a precisely defined fitness