SUMMARY

• GAs ARE RANDOMIZED

OPTIMIZATION PROCEDURES

• GAs ARE EASY TO LEARN AND IMPLEMENT

• GAs CAN BE BE APPLIED TO A WIDE

RANGE OF PROBLEMS

• GAs CAN BE COMBINED WITH NEURAL NETS

• GAs CAN BE USED TO AUTOMATICALLY

GENERATE CORRECT COMPUTER PROGRAMS

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