ví dụ, giảm phụ thuộc lẫn nhau, các thuật toán di truyền trở thành một lựa chọn khả thi. Mặc dù các vấn đề thiết lập ở cấp độ chiến thuật có phần dễ dàng hơn - đó là các biến phụ thuộc lẫn nhau ít hơn và đơn giản vấn đề tổ hợp các phương pháp phải được đáp ứng nhiều hơn. | DECISION-MAKING 121 natural state the problems are computationally hard to tackle but if we weaken our criterion for optimality by for example reducing interdependencies genetic algorithms become a viable option. Although the problem setting in the tactical level is somewhat easier - there are less interdependent variables and simpler combinatorial problems - the method must be more responsive. Owing to the computational demand inherent in making the method more responsive multiple search traces are not useful and we should devise heuristic search rules. The reactivity of the operational level dictates that we can only solve problems with a few variables or a simple objective function. Adaptation Adaptation can be defined as an ability to make appropriate responses to changed or changing circumstances. In a sense adaptation resembles learning a skill in the real world When we learn to ride a bike we do not receive for example the physical formulae describing the motions and forces involved. Instead we get simple - and possibly painful - feedback of success or failure. On the basis of this we adapt our behaviour and try again until we get it right. Generally speaking the difference between adaptation and optimization is that optimization searches for a solution for a given function whereas adaptation searches for a function behind given solutions see Figure . The assumption behind this is that the more the function adapts to the solution domain the better it corresponds to the originator of the modelled data. Adaptation is useful when the affecting factors or mechanisms behind the phenomena are unknown or dynamic. The downside is that we have to sample the search space to cover it sufficiently and the more dimensions . measured attributes it has the sparser our sample gets owing to combinatorial explosion. Since the task of pattern recognition is to abstract significant observations and rules from the given data it can be usually formed as an adaptation .
