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To solve for the best parameters, brute force optimization would require that 2,041 tests be performed; in TradeStation, that works out to about 56 minutes of computing time, extrapolating from the earlier illustration in which a small subset of the current solution space was examined. Only 1 minute of running time was required by the genetic optimizer; in an attempt to put it at a significant disadvantage, it was prematurely stopped after performing only 133 tests. The output from the genetic optimizer appears in Table 3-2. In this table, PI represents the period of the faster moving average, P2 the period of the slower moving average, NETthe total net profit, NETLNG the net profit for long positions, NETSiS the net profit for short positions, PFAC the profit factor, ROA% the annualized rehm on account, DRAW the maximum drawdown, TRDS the number of trades taken by the system, WIN% the percentage of winning trades, AVGT the profit or loss resulting from the average trade, and FZTthe fitness of the solution (which, in this instance, is merely the total net p&it). As with the brute force data in Table 3-1, the genetic data have been sorted by net profit (fitness) and only the 25 best solutions were presented.

A Web bene ts microsite where customers can come to see what they have earned and redeem points for rewards Try, if possible, to use this Web site for your loyalty program instead of live operators The customer service costs can eat up the pro ts from the program if you are not careful..

Comparison of the brute force and genetic optimization results (Tables 3- 1 and 3-2, respectively) reveals that the genetic optimizer isolated a solution with a greater net profit ($172,725) than did the brute force optimizer ($145,125). This is no surprise since a larger solution space, not decimated by increments, was explored. The surprise is that the better solution was found so quickly, despite the handicap of a prematurely stopped evolutionary process. Results like these demonstrate the incredible effectiveness of genetic optimization.

Reports to the members that are made available on the Web, or sent by email if your customers prefer it. If you send monthly statements, your report can be printed on the statement. Airlines send statements through the mail and have found them to be quite costly. They are all trying to shift to Web-based reporting. A clear expiration date that is announced when your program begins, so that if it is not working out, you can gracefully exit. You can always extend the deadline, but you can get in real trouble if you try to cancel a program while many customers are holding onto hard-earned points. A promotion plan that aims at a very high utilization rate. The place to start is with your employees. Enroll them in the plan from the start with special bene ts. You want all your employees to look the customer in the eye and say, You really should be in this program. It is great. I am in it myself. Promote the plan in every possible way. The purpose of the plan is to modify customer behavior. You can do that only if customers are enrolled. Status levels, if appropriate, with appropriate status rewards. The reason for status levels is to get customers to work hard to achieve them. The Platinum program for American was 50,000 miles in a calendar year. I used to go crazy trying to meet this goal. I would take two legs on an American ight when a direct ight was available on other airlines just so that I did not end the year with 46,000 miles. Let s look at some examples of nonairline loyalty programs.

Optimizers based on annealing mimic the thermodynamic process by which liquids freeze and metals anneal. Starting out at a high temperature, the atoms of a liquid or molten metal bounce rapidly about in a random fashion. Slowly cooled, they mange themselves into an orderly configuration-a crystal-that represents a minimal energy state for the system. Simulated in software, this thermodynamic process readily solves large-scale optimization problems. As with genetic opimization, optimization by simulared annealing is a very powerful Stochastic technique, modeled upon a natural phenomenon, that can find globally optimal solutions and handle ill-behaved fitness functions. Simulated annealing has effectively solved significant combinatorial problems, including

Most supermarkets today give their customers plastic frequent shopper cards. These are scanned at the checkout counter and permit the supermarket to build a valid database, giving it information on when and where you shop and how much you spend in each department. How do the supermarkets reward loyalty There are several ways: Few of them provide miles or points. The margins in supermarkets are too thin to allow them to give anything away.

the famous traveling salesman problem, and the problem of how best to arrange the millions of circuit elements found on modem integrated circuit chips, such as those that power computers. Methods based on simulated annealing should not be construed as limited to combinatorial optimization; they can readily be adapted to the optimization of real-valued parameters. Consequently, optimizers based on simulated annealing are applicable to a wide variety of problems, including those faced by traders. Since genetic optimizers perform so well, we have experienced little need to explore optimizers based on simulated annealing. In addition, there have been a few reports suggesting that, in many cases, annealing algorithms do not perform as well as genetic algorithms. Because of these reasons, we have not provided examples of simulated annealing and have little more to say about the method.