Search systematically examines feasible states, starting from an initial state and aiming for a goal. In optimization, it seeks feasible solutions that maximize or minimize an objective. section 1.1 section 1.5
Search involves balancing exploration of new regions against exploitation of regions where promising solutions have already been found. Whether to apply optimization also depends on the problem’s scale and the costs of implementation. section 1.5 section 1.1
A small delivery problem
A pizzeria making about ten deliveries on an average weeknight might consider route improvements, but the problem may be too small for those changes to produce noticeable benefits. section 1.1
Figure 1.10 Search dilemma—there is always a trade-off between branching out to new areas of the search space or focusing on an area with known good or elite solutions.
The figure illustrates the trade-off between exploring new search-space regions and focusing on regions with known good solutions. section 1.5
