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A costfuncrion works in exactly the same way, but higher numbers signify worse solutions The sum of the squared errors, commonly computed when working with linear regression or neural network models, is a cost function The best possible solution to a problem can now be defined: It is that particular solution that has the greatest fitness or the least cost Optimizers endeavor to find the best possible solution to a problem by maximizing fitness, as measured by a titness function, or minimizing cost, as computed by a cost function The best possible solution to a problem may be discovered in any number of ways Sometimes problems can be solved by simple trial-and-error, especially when guided by human insight into the problem being worked Alternatively, sophisticated procedures and algorithms may be necessary.

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shows the result of the 80 percent solution. You have converted a $1 million loss into a $1 million pro t increase by using an 80 percent solution. Where did the $3,500,000 annual cost for a warehouse come from Here is a simple way to measure data warehouse costs. If it costs you $6 million to build the warehouse, you can amortize these costs over 3 years, for $2 million per year. Maintenance of the warehouse will cost you at least $500,000 per year. Once the warehouse is built, you need to use it to communicate with your customers otherwise, what is the warehouse for Let s say that you spend only $1 million on customer communications per year. Then the warehouse will cost you at least $3,500,000 per year, as shown in Table 10-4. Whenever you or any of your colleagues at work come up with a proposal for a large direct response project, Web site, or warehouse, ask this question: How much could we save by concentrating on the easiest 80 percent and postponing the rest for later You will be amazed at the pro tability of the answers.

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For example, simulating the process of evolution (as genetic optimizers do) is a very powerful way to discover or evolve high-quality solutions to complex problems In some cases, the best problem solver is an analytic (calculus-based) procedure, such as a conjugate gradient Analytic optimization is an efficient approach for problems with smooth (differentiable) fitness surfaces, such as those encountered in training neural networks, developing multiple linear regression models, or computing simple-structure factor rotations HOW OPTIMIZERS ARE USED Optimizers are wonderful tools that can be used in a myriad of ways They help shape the aircraft we fly, design the cars we drive, and even select delivery routes.

There is another way of cutting the costs of customer management besides using the 80 percent rule. That is applying risk/revenue analysis. For this to work, you have to have done two things: 1. Determined the lifetime value of your customers and divided the customers into high, medium, and low lifetime value segments. 2. Determined the likelihood that customers will leave you and divided the customers into high, medium, and low likelihood of defection segments. When you have done this, you can develop a simple table that looks like Table 10-5.

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for our mail. Traders sometimes use optimizers to discover rule combinations that trade profitably. In Part II, we will demonstrate how a genetic optimizer can evolve profitable rule-based entry models. More commonly, traders call upon optimizers to determine the most appropriate values for system parameters; almost any kind of optimizer, except perhaps an analytic optimizer, may be employed for this purpose. Various kinds of optimizers, including powerful genetic algorithms, are effective for training or evolving neural or fuzzy logic networks. Asset allocation problems yield to appropriate optimization strategies. Sometimes it seems as if the only limit on how optimizers may be employed is the user s imagination, and therein lies a danger: It is easy to be seduced into optimizer abuse by the great and alluring power of this tool. The correct and incorrect applications of optimizers are discussed later in this chapter. TYPES OF OPTIMIZERS

Lifetime value High Medium Low High priority A priority B priority C Likelihood of Defection Medium Low priority B priority B priority C priority C priority C priority C

There are many kinds of optimizers, each with its own special strengths and weak nesses, advantages and disadvantages Optimizers can be classified along such dimensions as human versus machine, complex versus simple, special purpose versus general purpose, and analytic versus stochastic All optimizers-regardless of kind, efficiency, or reliability-execute a search for the best of many potential solutions to a formally specified problem lmpllcit Optimizers A mouse cannot be used to click on a button that says optimize There is no special command to enter In fact, there is no special software or even machine in sight Does this mean there is no optimizer No Even when there is no optimizer apparent, and it seems as though no optimization is going on, there is It is known as implicit optimization and works as follows: The trader tests a set of rules based upon some ideas regarding the market.

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